From 6647ef5e65f58242fbcea6c132269663c13a3690 Mon Sep 17 00:00:00 2001
From: wassname <wassname@users.noreply.github.com>
Date: Sun, 18 Oct 2020 15:33:14 +0800
Subject: [PATCH] multi datasets

---
 notebooks/RNN_Timeseries_Seq2Seq.ipynb | 2973 ++++++++++--------------
 notebooks/RNN_Timeseries_Seq2Seq.py    |   41 +-
 seq2seq_time/data/dataset.py           |    4 +-
 3 files changed, 1266 insertions(+), 1752 deletions(-)

diff --git a/notebooks/RNN_Timeseries_Seq2Seq.ipynb b/notebooks/RNN_Timeseries_Seq2Seq.ipynb
index c80270c..f59bf4e 100644
--- a/notebooks/RNN_Timeseries_Seq2Seq.ipynb
+++ b/notebooks/RNN_Timeseries_Seq2Seq.ipynb
@@ -37,8 +37,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:26.768622Z",
-     "start_time": "2020-10-18T06:50:26.349179Z"
+     "end_time": "2020-10-18T07:27:09.448780Z",
+     "start_time": "2020-10-18T07:27:09.018747Z"
     }
    },
    "outputs": [],
@@ -57,11 +57,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 51,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:27.825680Z",
-     "start_time": "2020-10-18T06:50:26.772253Z"
+     "end_time": "2020-10-18T07:31:56.528476Z",
+     "start_time": "2020-10-18T07:31:56.478567Z"
     }
    },
    "outputs": [],
@@ -77,6 +77,8 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "plt.rcParams['figure.figsize'] = (12.0, 4.0)\n",
+    "plt.style.use('ggplot')\n",
     "\n",
     "from pathlib import Path\n",
     "from tqdm.auto import tqdm\n",
@@ -86,11 +88,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:15:26.676538Z",
-     "start_time": "2020-10-18T07:15:26.624694Z"
+     "end_time": "2020-10-18T07:27:10.589899Z",
+     "start_time": "2020-10-18T07:27:10.503402Z"
     }
    },
    "outputs": [],
@@ -104,8 +106,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:27.942914Z",
-     "start_time": "2020-10-18T06:50:27.916544Z"
+     "end_time": "2020-10-18T07:27:10.619540Z",
+     "start_time": "2020-10-18T07:27:10.592972Z"
     }
    },
    "outputs": [],
@@ -131,8 +133,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:27.994550Z",
-     "start_time": "2020-10-18T06:50:27.945293Z"
+     "end_time": "2020-10-18T07:27:10.661223Z",
+     "start_time": "2020-10-18T07:27:10.621920Z"
     }
    },
    "outputs": [
@@ -169,8 +171,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:28.039589Z",
-     "start_time": "2020-10-18T06:50:27.996675Z"
+     "end_time": "2020-10-18T07:27:10.702018Z",
+     "start_time": "2020-10-18T07:27:10.663465Z"
     },
     "lines_to_next_cell": 0
    },
@@ -243,11 +245,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:34.310763Z",
-     "start_time": "2020-10-18T07:17:29.216045Z"
+     "end_time": "2020-10-18T07:27:14.677877Z",
+     "start_time": "2020-10-18T07:27:10.704252Z"
     },
     "scrolled": true
    },
@@ -269,21 +271,35 @@
     "# df = df.resample(freq).first().dropna() # Where empty we will backfill, this will respect causality, and mostly maintain the mean\n",
     "\n",
     "# df = df.tail(int(max_rows)).copy() # Just use last X rows\n",
-    "# df = pd.concat(dfs, 0)\n",
-    "df = dfs[0]"
+    "df = pd.concat(dfs[:6], 0)\n",
+    "# df = dfs[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:16:47.239423Z",
-     "start_time": "2020-10-18T07:16:46.591063Z"
+     "end_time": "2020-10-18T07:27:14.722131Z",
+     "start_time": "2020-10-18T07:27:14.681417Z"
     }
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['MAC000002', 'MAC000246', 'MAC000450', 'MAC001074', 'MAC003223',\n",
+       "       'MAC003239'], dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.LCLid.unique()"
+   ]
   },
   {
    "cell_type": "code",
@@ -299,11 +315,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:34.425234Z",
-     "start_time": "2020-10-18T07:17:34.313442Z"
+     "end_time": "2020-10-18T07:27:14.905468Z",
+     "start_time": "2020-10-18T07:27:14.724956Z"
     }
    },
    "outputs": [
@@ -349,64 +365,64 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
-       "      <td>24119.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.00000</td>\n",
+       "      <td>144861.000000</td>\n",
+       "      <td>144861.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>0.252733</td>\n",
-       "      <td>6.672416</td>\n",
-       "      <td>15.899623</td>\n",
-       "      <td>27.084746</td>\n",
-       "      <td>11.504664</td>\n",
-       "      <td>14.999378</td>\n",
-       "      <td>2.999129</td>\n",
-       "      <td>11.132308</td>\n",
-       "      <td>193.259215</td>\n",
-       "      <td>9.896434</td>\n",
-       "      <td>6.069227</td>\n",
-       "      <td>1012.490613</td>\n",
-       "      <td>8.446964</td>\n",
-       "      <td>4.000070</td>\n",
-       "      <td>0.786521</td>\n",
-       "      <td>0.021891</td>\n",
+       "      <td>0.480475</td>\n",
+       "      <td>6.702660</td>\n",
+       "      <td>15.803764</td>\n",
+       "      <td>27.250626</td>\n",
+       "      <td>11.505257</td>\n",
+       "      <td>14.999482</td>\n",
+       "      <td>2.997335</td>\n",
+       "      <td>11.223563</td>\n",
+       "      <td>194.106060</td>\n",
+       "      <td>10.408324</td>\n",
+       "      <td>6.499879</td>\n",
+       "      <td>1012.888314</td>\n",
+       "      <td>9.123004</td>\n",
+       "      <td>3.93198</td>\n",
+       "      <td>0.783605</td>\n",
+       "      <td>0.021869</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>0.247079</td>\n",
-       "      <td>3.881916</td>\n",
-       "      <td>8.744709</td>\n",
-       "      <td>16.913482</td>\n",
-       "      <td>6.922411</td>\n",
-       "      <td>15.000311</td>\n",
-       "      <td>2.002766</td>\n",
-       "      <td>3.143717</td>\n",
-       "      <td>92.104725</td>\n",
-       "      <td>5.940428</td>\n",
-       "      <td>5.195058</td>\n",
-       "      <td>11.420208</td>\n",
-       "      <td>7.175969</td>\n",
-       "      <td>2.103774</td>\n",
-       "      <td>0.138358</td>\n",
-       "      <td>0.146332</td>\n",
+       "      <td>0.608599</td>\n",
+       "      <td>3.673558</td>\n",
+       "      <td>8.776667</td>\n",
+       "      <td>15.993510</td>\n",
+       "      <td>6.922274</td>\n",
+       "      <td>15.000052</td>\n",
+       "      <td>2.000109</td>\n",
+       "      <td>3.041926</td>\n",
+       "      <td>91.403667</td>\n",
+       "      <td>5.933314</td>\n",
+       "      <td>5.175187</td>\n",
+       "      <td>11.071921</td>\n",
+       "      <td>7.143647</td>\n",
+       "      <td>2.03807</td>\n",
+       "      <td>0.140842</td>\n",
+       "      <td>0.146257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>0.065000</td>\n",
+       "      <td>0.014000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
@@ -415,74 +431,74 @@
        "      <td>0.000000</td>\n",
        "      <td>0.270000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-3.860000</td>\n",
-       "      <td>-8.920000</td>\n",
+       "      <td>-5.640000</td>\n",
+       "      <td>-9.980000</td>\n",
        "      <td>975.740000</td>\n",
        "      <td>-8.880000</td>\n",
-       "      <td>0.040000</td>\n",
+       "      <td>0.04000</td>\n",
        "      <td>0.230000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>0.112000</td>\n",
+       "      <td>0.096000</td>\n",
        "      <td>3.000000</td>\n",
        "      <td>8.000000</td>\n",
-       "      <td>10.000000</td>\n",
+       "      <td>13.000000</td>\n",
        "      <td>6.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>9.960000</td>\n",
-       "      <td>115.000000</td>\n",
-       "      <td>5.640000</td>\n",
-       "      <td>2.240000</td>\n",
-       "      <td>1005.470000</td>\n",
-       "      <td>2.980000</td>\n",
-       "      <td>2.450000</td>\n",
-       "      <td>0.710000</td>\n",
+       "      <td>10.220000</td>\n",
+       "      <td>117.000000</td>\n",
+       "      <td>6.260000</td>\n",
+       "      <td>2.670000</td>\n",
+       "      <td>1006.460000</td>\n",
+       "      <td>3.590000</td>\n",
+       "      <td>2.44000</td>\n",
+       "      <td>0.700000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>0.158000</td>\n",
+       "      <td>0.218000</td>\n",
        "      <td>7.000000</td>\n",
        "      <td>16.000000</td>\n",
        "      <td>28.000000</td>\n",
        "      <td>12.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>3.000000</td>\n",
-       "      <td>12.130000</td>\n",
-       "      <td>214.000000</td>\n",
-       "      <td>9.140000</td>\n",
-       "      <td>6.040000</td>\n",
-       "      <td>1013.450000</td>\n",
-       "      <td>7.120000</td>\n",
-       "      <td>3.760000</td>\n",
-       "      <td>0.820000</td>\n",
+       "      <td>12.260000</td>\n",
+       "      <td>215.000000</td>\n",
+       "      <td>9.870000</td>\n",
+       "      <td>6.610000</td>\n",
+       "      <td>1013.660000</td>\n",
+       "      <td>8.990000</td>\n",
+       "      <td>3.70000</td>\n",
+       "      <td>0.810000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>0.286000</td>\n",
+       "      <td>0.640000</td>\n",
        "      <td>10.000000</td>\n",
        "      <td>23.000000</td>\n",
-       "      <td>43.000000</td>\n",
+       "      <td>42.000000</td>\n",
        "      <td>18.000000</td>\n",
        "      <td>30.000000</td>\n",
        "      <td>5.000000</td>\n",
-       "      <td>13.070000</td>\n",
+       "      <td>13.080000</td>\n",
        "      <td>255.000000</td>\n",
-       "      <td>13.670000</td>\n",
-       "      <td>9.840000</td>\n",
-       "      <td>1020.550000</td>\n",
-       "      <td>13.670000</td>\n",
-       "      <td>5.200000</td>\n",
+       "      <td>14.440000</td>\n",
+       "      <td>10.460000</td>\n",
+       "      <td>1020.340000</td>\n",
+       "      <td>14.440000</td>\n",
+       "      <td>5.13000</td>\n",
        "      <td>0.890000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>2.994000</td>\n",
+       "      <td>5.250000</td>\n",
        "      <td>12.000000</td>\n",
        "      <td>31.000000</td>\n",
        "      <td>52.000000</td>\n",
@@ -492,10 +508,10 @@
        "      <td>16.090000</td>\n",
        "      <td>359.000000</td>\n",
        "      <td>32.400000</td>\n",
-       "      <td>18.950000</td>\n",
-       "      <td>1040.130000</td>\n",
+       "      <td>19.880000</td>\n",
+       "      <td>1043.320000</td>\n",
        "      <td>32.420000</td>\n",
-       "      <td>14.800000</td>\n",
+       "      <td>14.80000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -504,48 +520,48 @@
        "</div>"
       ],
       "text/plain": [
-       "       energy(kWh/hh)         month           day          week          hour  \\\n",
-       "count    24119.000000  24119.000000  24119.000000  24119.000000  24119.000000   \n",
-       "mean         0.252733      6.672416     15.899623     27.084746     11.504664   \n",
-       "std          0.247079      3.881916      8.744709     16.913482      6.922411   \n",
-       "min          0.065000      1.000000      1.000000      1.000000      0.000000   \n",
-       "25%          0.112000      3.000000      8.000000     10.000000      6.000000   \n",
-       "50%          0.158000      7.000000     16.000000     28.000000     12.000000   \n",
-       "75%          0.286000     10.000000     23.000000     43.000000     18.000000   \n",
-       "max          2.994000     12.000000     31.000000     52.000000     23.000000   \n",
+       "       energy(kWh/hh)          month            day           week  \\\n",
+       "count   144861.000000  144861.000000  144861.000000  144861.000000   \n",
+       "mean         0.480475       6.702660      15.803764      27.250626   \n",
+       "std          0.608599       3.673558       8.776667      15.993510   \n",
+       "min          0.014000       1.000000       1.000000       1.000000   \n",
+       "25%          0.096000       3.000000       8.000000      13.000000   \n",
+       "50%          0.218000       7.000000      16.000000      28.000000   \n",
+       "75%          0.640000      10.000000      23.000000      42.000000   \n",
+       "max          5.250000      12.000000      31.000000      52.000000   \n",
        "\n",
-       "             minute     dayofweek    visibility   windBearing   temperature  \\\n",
-       "count  24119.000000  24119.000000  24119.000000  24119.000000  24119.000000   \n",
-       "mean      14.999378      2.999129     11.132308    193.259215      9.896434   \n",
-       "std       15.000311      2.002766      3.143717     92.104725      5.940428   \n",
-       "min        0.000000      0.000000      0.270000      0.000000     -3.860000   \n",
-       "25%        0.000000      1.000000      9.960000    115.000000      5.640000   \n",
-       "50%        0.000000      3.000000     12.130000    214.000000      9.140000   \n",
-       "75%       30.000000      5.000000     13.070000    255.000000     13.670000   \n",
-       "max       30.000000      6.000000     16.090000    359.000000     32.400000   \n",
+       "                hour         minute      dayofweek     visibility  \\\n",
+       "count  144861.000000  144861.000000  144861.000000  144861.000000   \n",
+       "mean       11.505257      14.999482       2.997335      11.223563   \n",
+       "std         6.922274      15.000052       2.000109       3.041926   \n",
+       "min         0.000000       0.000000       0.000000       0.270000   \n",
+       "25%         6.000000       0.000000       1.000000      10.220000   \n",
+       "50%        12.000000       0.000000       3.000000      12.260000   \n",
+       "75%        18.000000      30.000000       5.000000      13.080000   \n",
+       "max        23.000000      30.000000       6.000000      16.090000   \n",
        "\n",
-       "           dewPoint      pressure  apparentTemperature     windSpeed  \\\n",
-       "count  24119.000000  24119.000000         24119.000000  24119.000000   \n",
-       "mean       6.069227   1012.490613             8.446964      4.000070   \n",
-       "std        5.195058     11.420208             7.175969      2.103774   \n",
-       "min       -8.920000    975.740000            -8.880000      0.040000   \n",
-       "25%        2.240000   1005.470000             2.980000      2.450000   \n",
-       "50%        6.040000   1013.450000             7.120000      3.760000   \n",
-       "75%        9.840000   1020.550000            13.670000      5.200000   \n",
-       "max       18.950000   1040.130000            32.420000     14.800000   \n",
+       "         windBearing    temperature       dewPoint       pressure  \\\n",
+       "count  144861.000000  144861.000000  144861.000000  144861.000000   \n",
+       "mean      194.106060      10.408324       6.499879    1012.888314   \n",
+       "std        91.403667       5.933314       5.175187      11.071921   \n",
+       "min         0.000000      -5.640000      -9.980000     975.740000   \n",
+       "25%       117.000000       6.260000       2.670000    1006.460000   \n",
+       "50%       215.000000       9.870000       6.610000    1013.660000   \n",
+       "75%       255.000000      14.440000      10.460000    1020.340000   \n",
+       "max       359.000000      32.400000      19.880000    1043.320000   \n",
        "\n",
-       "           humidity       holiday  \n",
-       "count  24119.000000  24119.000000  \n",
-       "mean       0.786521      0.021891  \n",
-       "std        0.138358      0.146332  \n",
-       "min        0.230000      0.000000  \n",
-       "25%        0.710000      0.000000  \n",
-       "50%        0.820000      0.000000  \n",
-       "75%        0.890000      0.000000  \n",
-       "max        1.000000      1.000000  "
+       "       apparentTemperature     windSpeed       humidity        holiday  \n",
+       "count        144861.000000  144861.00000  144861.000000  144861.000000  \n",
+       "mean              9.123004       3.93198       0.783605       0.021869  \n",
+       "std               7.143647       2.03807       0.140842       0.146257  \n",
+       "min              -8.880000       0.04000       0.230000       0.000000  \n",
+       "25%               3.590000       2.44000       0.700000       0.000000  \n",
+       "50%               8.990000       3.70000       0.810000       0.000000  \n",
+       "75%              14.440000       5.13000       0.890000       0.000000  \n",
+       "max              32.420000      14.80000       1.000000       1.000000  "
       ]
      },
-     "execution_count": 143,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,11 +572,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 25,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:34.566532Z",
-     "start_time": "2020-10-18T07:17:34.428092Z"
+     "end_time": "2020-10-18T07:28:31.172955Z",
+     "start_time": "2020-10-18T07:28:30.773541Z"
     }
    },
    "outputs": [
@@ -601,121 +617,121 @@
        "      <th>windSpeed</th>\n",
        "      <th>humidity</th>\n",
        "      <th>holiday</th>\n",
-       "      <th>LCLid</th>\n",
        "      <th>stdorToU</th>\n",
+       "      <th>LCLid</th>\n",
        "      <th>Acorn_grouped</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-10-12 11:30:00</th>\n",
-       "      <td>-0.444130</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>-0.072904</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.800636</td>\n",
-       "      <td>0.453439</td>\n",
-       "      <td>-0.305918</td>\n",
-       "      <td>-0.458899</td>\n",
-       "      <td>0.577361</td>\n",
-       "      <td>1.406991</td>\n",
-       "      <td>-1.492695</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.554513</td>\n",
+       "      <td>0.897591</td>\n",
+       "      <td>-0.433397</td>\n",
+       "      <td>0.859688</td>\n",
+       "      <td>-0.072990</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>0.501307</td>\n",
+       "      <td>0.754931</td>\n",
+       "      <td>0.797497</td>\n",
+       "      <td>0.367701</td>\n",
+       "      <td>-0.390302</td>\n",
+       "      <td>-0.509246</td>\n",
+       "      <td>0.485327</td>\n",
+       "      <td>1.485734</td>\n",
+       "      <td>-1.445630</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-12 12:00:00</th>\n",
-       "      <td>1.660500</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>0.071557</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.724633</td>\n",
-       "      <td>0.667233</td>\n",
-       "      <td>-0.336717</td>\n",
-       "      <td>-0.448391</td>\n",
-       "      <td>0.754344</td>\n",
-       "      <td>1.387977</td>\n",
-       "      <td>-1.854084</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.299911</td>\n",
+       "      <td>0.897591</td>\n",
+       "      <td>-0.433397</td>\n",
+       "      <td>0.859688</td>\n",
+       "      <td>0.071471</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>0.501307</td>\n",
+       "      <td>0.754931</td>\n",
+       "      <td>0.720914</td>\n",
+       "      <td>0.581747</td>\n",
+       "      <td>-0.421219</td>\n",
+       "      <td>-0.498408</td>\n",
+       "      <td>0.663108</td>\n",
+       "      <td>1.466108</td>\n",
+       "      <td>-1.800639</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-12 12:30:00</th>\n",
-       "      <td>0.013222</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>0.071557</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.724633</td>\n",
-       "      <td>0.667233</td>\n",
-       "      <td>-0.336717</td>\n",
-       "      <td>-0.448391</td>\n",
-       "      <td>0.754344</td>\n",
-       "      <td>1.387977</td>\n",
-       "      <td>-1.854084</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.368840</td>\n",
+       "      <td>0.897591</td>\n",
+       "      <td>-0.433397</td>\n",
+       "      <td>0.859688</td>\n",
+       "      <td>0.071471</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>0.501307</td>\n",
+       "      <td>0.754931</td>\n",
+       "      <td>0.720914</td>\n",
+       "      <td>0.581747</td>\n",
+       "      <td>-0.421219</td>\n",
+       "      <td>-0.498408</td>\n",
+       "      <td>0.663108</td>\n",
+       "      <td>1.466108</td>\n",
+       "      <td>-1.800639</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-12 13:00:00</th>\n",
-       "      <td>-0.395562</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>0.216018</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.648631</td>\n",
-       "      <td>0.689117</td>\n",
-       "      <td>-0.305918</td>\n",
-       "      <td>-0.433505</td>\n",
-       "      <td>0.772460</td>\n",
-       "      <td>1.426005</td>\n",
-       "      <td>-1.854084</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.534795</td>\n",
+       "      <td>0.897591</td>\n",
+       "      <td>-0.433397</td>\n",
+       "      <td>0.859688</td>\n",
+       "      <td>0.215933</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>0.501307</td>\n",
+       "      <td>0.754931</td>\n",
+       "      <td>0.644330</td>\n",
+       "      <td>0.603657</td>\n",
+       "      <td>-0.390302</td>\n",
+       "      <td>-0.483054</td>\n",
+       "      <td>0.681306</td>\n",
+       "      <td>1.505361</td>\n",
+       "      <td>-1.800639</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-10-12 13:30:00</th>\n",
-       "      <td>-0.217478</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>0.216018</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.648631</td>\n",
-       "      <td>0.689117</td>\n",
-       "      <td>-0.305918</td>\n",
-       "      <td>-0.433505</td>\n",
-       "      <td>0.772460</td>\n",
-       "      <td>1.426005</td>\n",
-       "      <td>-1.854084</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.462498</td>\n",
+       "      <td>0.897591</td>\n",
+       "      <td>-0.433397</td>\n",
+       "      <td>0.859688</td>\n",
+       "      <td>0.215933</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>0.501307</td>\n",
+       "      <td>0.754931</td>\n",
+       "      <td>0.644330</td>\n",
+       "      <td>0.603657</td>\n",
+       "      <td>-0.390302</td>\n",
+       "      <td>-0.483054</td>\n",
+       "      <td>0.681306</td>\n",
+       "      <td>1.505361</td>\n",
+       "      <td>-1.800639</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -741,189 +757,189 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27 22:00:00</th>\n",
-       "      <td>0.660800</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.269408</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>1.516170</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.000435</td>\n",
-       "      <td>0.912217</td>\n",
-       "      <td>0.246907</td>\n",
-       "      <td>-0.975781</td>\n",
-       "      <td>-0.852602</td>\n",
-       "      <td>-0.597253</td>\n",
-       "      <td>-0.980649</td>\n",
-       "      <td>-0.465873</td>\n",
-       "      <td>0.386532</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.521650</td>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>1.516088</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>0.001332</td>\n",
+       "      <td>0.912726</td>\n",
+       "      <td>0.239531</td>\n",
+       "      <td>-1.063208</td>\n",
+       "      <td>-0.939076</td>\n",
+       "      <td>-0.651950</td>\n",
+       "      <td>-1.079705</td>\n",
+       "      <td>-0.447474</td>\n",
+       "      <td>0.400416</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27 22:30:00</th>\n",
-       "      <td>4.441040</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.269408</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>1.516170</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.000435</td>\n",
-       "      <td>0.912217</td>\n",
-       "      <td>0.246907</td>\n",
-       "      <td>-0.975781</td>\n",
-       "      <td>-0.852602</td>\n",
-       "      <td>-0.597253</td>\n",
-       "      <td>-0.980649</td>\n",
-       "      <td>-0.465873</td>\n",
-       "      <td>0.386532</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.120728</td>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>1.516088</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>0.001332</td>\n",
+       "      <td>0.912726</td>\n",
+       "      <td>0.239531</td>\n",
+       "      <td>-1.063208</td>\n",
+       "      <td>-0.939076</td>\n",
+       "      <td>-0.651950</td>\n",
+       "      <td>-1.079705</td>\n",
+       "      <td>-0.447474</td>\n",
+       "      <td>0.400416</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27 23:00:00</th>\n",
-       "      <td>4.024161</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.269408</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>1.660631</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.000435</td>\n",
-       "      <td>0.921760</td>\n",
-       "      <td>0.073188</td>\n",
-       "      <td>-1.004399</td>\n",
-       "      <td>-0.858377</td>\n",
-       "      <td>-0.689197</td>\n",
-       "      <td>-0.979256</td>\n",
-       "      <td>-0.594216</td>\n",
-       "      <td>0.458810</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.653100</td>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>1.660550</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>0.001332</td>\n",
+       "      <td>0.922589</td>\n",
+       "      <td>0.064483</td>\n",
+       "      <td>-1.091860</td>\n",
+       "      <td>-0.944873</td>\n",
+       "      <td>-0.746785</td>\n",
+       "      <td>-1.078305</td>\n",
+       "      <td>-0.579953</td>\n",
+       "      <td>0.471418</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-27 23:30:00</th>\n",
-       "      <td>3.906788</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.269408</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>1.660631</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.000435</td>\n",
-       "      <td>0.921760</td>\n",
-       "      <td>0.073188</td>\n",
-       "      <td>-1.004399</td>\n",
-       "      <td>-0.858377</td>\n",
-       "      <td>-0.689197</td>\n",
-       "      <td>-0.979256</td>\n",
-       "      <td>-0.594216</td>\n",
-       "      <td>0.458810</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.692535</td>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>1.660550</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>0.001332</td>\n",
+       "      <td>0.922589</td>\n",
+       "      <td>0.064483</td>\n",
+       "      <td>-1.091860</td>\n",
+       "      <td>-0.944873</td>\n",
+       "      <td>-0.746785</td>\n",
+       "      <td>-1.078305</td>\n",
+       "      <td>-0.579953</td>\n",
+       "      <td>0.471418</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-02-28 00:00:00</th>\n",
-       "      <td>4.590792</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.383766</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>-1.661979</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.476418</td>\n",
-       "      <td>-0.035387</td>\n",
-       "      <td>-1.024600</td>\n",
-       "      <td>-0.873777</td>\n",
-       "      <td>-0.781141</td>\n",
-       "      <td>-0.972288</td>\n",
-       "      <td>-0.698792</td>\n",
-       "      <td>0.458810</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.643242</td>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.389625</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>-1.662069</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>0.501307</td>\n",
+       "      <td>0.462352</td>\n",
+       "      <td>-0.044922</td>\n",
+       "      <td>-1.112085</td>\n",
+       "      <td>-0.960332</td>\n",
+       "      <td>-0.841620</td>\n",
+       "      <td>-1.071306</td>\n",
+       "      <td>-0.687898</td>\n",
+       "      <td>0.471418</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>24119 rows × 19 columns</p>\n",
+       "<p>144861 rows × 19 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                     energy(kWh/hh)     month       day      week      hour  \\\n",
-       "2012-10-12 11:30:00       -0.444130  0.857219 -0.445950  0.822749 -0.072904   \n",
-       "2012-10-12 12:00:00        1.660500  0.857219 -0.445950  0.822749  0.071557   \n",
-       "2012-10-12 12:30:00        0.013222  0.857219 -0.445950  0.822749  0.071557   \n",
-       "2012-10-12 13:00:00       -0.395562  0.857219 -0.445950  0.822749  0.216018   \n",
-       "2012-10-12 13:30:00       -0.217478  0.857219 -0.445950  0.822749  0.216018   \n",
+       "2012-10-12 11:30:00       -0.554513  0.897591 -0.433397  0.859688 -0.072990   \n",
+       "2012-10-12 12:00:00        0.299911  0.897591 -0.433397  0.859688  0.071471   \n",
+       "2012-10-12 12:30:00       -0.368840  0.897591 -0.433397  0.859688  0.071471   \n",
+       "2012-10-12 13:00:00       -0.534795  0.897591 -0.433397  0.859688  0.215933   \n",
+       "2012-10-12 13:30:00       -0.462498  0.897591 -0.433397  0.859688  0.215933   \n",
        "...                             ...       ...       ...       ...       ...   \n",
-       "2014-02-27 22:00:00        0.660800 -1.203661  1.269408 -1.069273  1.516170   \n",
-       "2014-02-27 22:30:00        4.441040 -1.203661  1.269408 -1.069273  1.516170   \n",
-       "2014-02-27 23:00:00        4.024161 -1.203661  1.269408 -1.069273  1.660631   \n",
-       "2014-02-27 23:30:00        3.906788 -1.203661  1.269408 -1.069273  1.660631   \n",
-       "2014-02-28 00:00:00        4.590792 -1.203661  1.383766 -1.069273 -1.661979   \n",
+       "2014-02-27 22:00:00       -0.521650 -1.280142  1.275686 -1.141131  1.516088   \n",
+       "2014-02-27 22:30:00       -0.120728 -1.280142  1.275686 -1.141131  1.516088   \n",
+       "2014-02-27 23:00:00       -0.653100 -1.280142  1.275686 -1.141131  1.660550   \n",
+       "2014-02-27 23:30:00       -0.692535 -1.280142  1.275686 -1.141131  1.660550   \n",
+       "2014-02-28 00:00:00       -0.643242 -1.280142  1.389625 -1.141131 -1.662069   \n",
        "\n",
        "                       minute  dayofweek  visibility  windBearing  \\\n",
-       "2012-10-12 11:30:00  1.000041   0.499755    0.759528     0.800636   \n",
-       "2012-10-12 12:00:00 -0.999959   0.499755    0.759528     0.724633   \n",
-       "2012-10-12 12:30:00  1.000041   0.499755    0.759528     0.724633   \n",
-       "2012-10-12 13:00:00 -0.999959   0.499755    0.759528     0.648631   \n",
-       "2012-10-12 13:30:00  1.000041   0.499755    0.759528     0.648631   \n",
+       "2012-10-12 11:30:00  1.000035   0.501307    0.754931     0.797497   \n",
+       "2012-10-12 12:00:00 -0.999965   0.501307    0.754931     0.720914   \n",
+       "2012-10-12 12:30:00  1.000035   0.501307    0.754931     0.720914   \n",
+       "2012-10-12 13:00:00 -0.999965   0.501307    0.754931     0.644330   \n",
+       "2012-10-12 13:30:00  1.000035   0.501307    0.754931     0.644330   \n",
        "...                       ...        ...         ...          ...   \n",
-       "2014-02-27 22:00:00 -0.999959   0.000435    0.912217     0.246907   \n",
-       "2014-02-27 22:30:00  1.000041   0.000435    0.912217     0.246907   \n",
-       "2014-02-27 23:00:00 -0.999959   0.000435    0.921760     0.073188   \n",
-       "2014-02-27 23:30:00  1.000041   0.000435    0.921760     0.073188   \n",
-       "2014-02-28 00:00:00 -0.999959   0.499755    0.476418    -0.035387   \n",
+       "2014-02-27 22:00:00 -0.999965   0.001332    0.912726     0.239531   \n",
+       "2014-02-27 22:30:00  1.000035   0.001332    0.912726     0.239531   \n",
+       "2014-02-27 23:00:00 -0.999965   0.001332    0.922589     0.064483   \n",
+       "2014-02-27 23:30:00  1.000035   0.001332    0.922589     0.064483   \n",
+       "2014-02-28 00:00:00 -0.999965   0.501307    0.462352    -0.044922   \n",
        "\n",
        "                     temperature  dewPoint  pressure  apparentTemperature  \\\n",
-       "2012-10-12 11:30:00     0.453439 -0.305918 -0.458899             0.577361   \n",
-       "2012-10-12 12:00:00     0.667233 -0.336717 -0.448391             0.754344   \n",
-       "2012-10-12 12:30:00     0.667233 -0.336717 -0.448391             0.754344   \n",
-       "2012-10-12 13:00:00     0.689117 -0.305918 -0.433505             0.772460   \n",
-       "2012-10-12 13:30:00     0.689117 -0.305918 -0.433505             0.772460   \n",
+       "2012-10-12 11:30:00     0.367701 -0.390302 -0.509246             0.485327   \n",
+       "2012-10-12 12:00:00     0.581747 -0.421219 -0.498408             0.663108   \n",
+       "2012-10-12 12:30:00     0.581747 -0.421219 -0.498408             0.663108   \n",
+       "2012-10-12 13:00:00     0.603657 -0.390302 -0.483054             0.681306   \n",
+       "2012-10-12 13:30:00     0.603657 -0.390302 -0.483054             0.681306   \n",
        "...                          ...       ...       ...                  ...   \n",
-       "2014-02-27 22:00:00    -0.975781 -0.852602 -0.597253            -0.980649   \n",
-       "2014-02-27 22:30:00    -0.975781 -0.852602 -0.597253            -0.980649   \n",
-       "2014-02-27 23:00:00    -1.004399 -0.858377 -0.689197            -0.979256   \n",
-       "2014-02-27 23:30:00    -1.004399 -0.858377 -0.689197            -0.979256   \n",
-       "2014-02-28 00:00:00    -1.024600 -0.873777 -0.781141            -0.972288   \n",
+       "2014-02-27 22:00:00    -1.063208 -0.939076 -0.651950            -1.079705   \n",
+       "2014-02-27 22:30:00    -1.063208 -0.939076 -0.651950            -1.079705   \n",
+       "2014-02-27 23:00:00    -1.091860 -0.944873 -0.746785            -1.078305   \n",
+       "2014-02-27 23:30:00    -1.091860 -0.944873 -0.746785            -1.078305   \n",
+       "2014-02-28 00:00:00    -1.112085 -0.960332 -0.841620            -1.071306   \n",
        "\n",
-       "                     windSpeed  humidity   holiday  LCLid  stdorToU  \\\n",
-       "2012-10-12 11:30:00   1.406991 -1.492695 -0.149604    0.0       0.0   \n",
-       "2012-10-12 12:00:00   1.387977 -1.854084 -0.149604    0.0       0.0   \n",
-       "2012-10-12 12:30:00   1.387977 -1.854084 -0.149604    0.0       0.0   \n",
-       "2012-10-12 13:00:00   1.426005 -1.854084 -0.149604    0.0       0.0   \n",
-       "2012-10-12 13:30:00   1.426005 -1.854084 -0.149604    0.0       0.0   \n",
-       "...                        ...       ...       ...    ...       ...   \n",
-       "2014-02-27 22:00:00  -0.465873  0.386532 -0.149604    0.0       0.0   \n",
-       "2014-02-27 22:30:00  -0.465873  0.386532 -0.149604    0.0       0.0   \n",
-       "2014-02-27 23:00:00  -0.594216  0.458810 -0.149604    0.0       0.0   \n",
-       "2014-02-27 23:30:00  -0.594216  0.458810 -0.149604    0.0       0.0   \n",
-       "2014-02-28 00:00:00  -0.698792  0.458810 -0.149604    0.0       0.0   \n",
+       "                     windSpeed  humidity   holiday  stdorToU  LCLid  \\\n",
+       "2012-10-12 11:30:00   1.485734 -1.445630 -0.149527       0.0    0.0   \n",
+       "2012-10-12 12:00:00   1.466108 -1.800639 -0.149527       0.0    0.0   \n",
+       "2012-10-12 12:30:00   1.466108 -1.800639 -0.149527       0.0    0.0   \n",
+       "2012-10-12 13:00:00   1.505361 -1.800639 -0.149527       0.0    0.0   \n",
+       "2012-10-12 13:30:00   1.505361 -1.800639 -0.149527       0.0    0.0   \n",
+       "...                        ...       ...       ...       ...    ...   \n",
+       "2014-02-27 22:00:00  -0.447474  0.400416 -0.149527       0.0    5.0   \n",
+       "2014-02-27 22:30:00  -0.447474  0.400416 -0.149527       0.0    5.0   \n",
+       "2014-02-27 23:00:00  -0.579953  0.471418 -0.149527       0.0    5.0   \n",
+       "2014-02-27 23:30:00  -0.579953  0.471418 -0.149527       0.0    5.0   \n",
+       "2014-02-28 00:00:00  -0.687898  0.471418 -0.149527       0.0    5.0   \n",
        "\n",
        "                     Acorn_grouped  \n",
-       "2012-10-12 11:30:00            0.0  \n",
-       "2012-10-12 12:00:00            0.0  \n",
-       "2012-10-12 12:30:00            0.0  \n",
-       "2012-10-12 13:00:00            0.0  \n",
-       "2012-10-12 13:30:00            0.0  \n",
+       "2012-10-12 11:30:00            1.0  \n",
+       "2012-10-12 12:00:00            1.0  \n",
+       "2012-10-12 12:30:00            1.0  \n",
+       "2012-10-12 13:00:00            1.0  \n",
+       "2012-10-12 13:30:00            1.0  \n",
        "...                            ...  \n",
-       "2014-02-27 22:00:00            0.0  \n",
-       "2014-02-27 22:30:00            0.0  \n",
-       "2014-02-27 23:00:00            0.0  \n",
-       "2014-02-27 23:30:00            0.0  \n",
-       "2014-02-28 00:00:00            0.0  \n",
+       "2014-02-27 22:00:00            1.0  \n",
+       "2014-02-27 22:30:00            1.0  \n",
+       "2014-02-27 23:00:00            1.0  \n",
+       "2014-02-27 23:30:00            1.0  \n",
+       "2014-02-28 00:00:00            1.0  \n",
        "\n",
-       "[24119 rows x 19 columns]"
+       "[144861 rows x 19 columns]"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -947,11 +963,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:34.619990Z",
-     "start_time": "2020-10-18T07:17:34.569878Z"
+     "end_time": "2020-10-18T07:28:31.210067Z",
+     "start_time": "2020-10-18T07:28:31.174849Z"
     }
    },
    "outputs": [
@@ -961,7 +977,7 @@
        "StandardScaler()"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -973,26 +989,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 30,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:34.678037Z",
-     "start_time": "2020-10-18T07:17:34.622358Z"
+     "end_time": "2020-10-18T07:28:36.430829Z",
+     "start_time": "2020-10-18T07:28:36.250590Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# # Resample\n",
-    "df_norm = df_norm.resample(freq).first().fillna(0)"
+    "dfs_norm = [d.resample(freq).first().ffill().dropna() for _, d in df_norm.groupby('LCLid')]\n",
+    "len(dfs_norm)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 31,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:37.886746Z",
-     "start_time": "2020-10-18T07:17:34.679999Z"
+     "end_time": "2020-10-18T07:28:37.155841Z",
+     "start_time": "2020-10-18T07:28:36.474491Z"
     },
     "lines_to_next_cell": 0
    },
@@ -1000,16 +1027,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f04a1f2bf90>"
+       "<matplotlib.legend.Legend at 0x7f8fd7d85f90>"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1022,395 +1049,29 @@
    ],
    "source": [
     "# split data, with the test in the future\n",
-    "n_split = -int(len(df)*0.2)\n",
-    "df_train = df_norm[:n_split]\n",
-    "df_test = df_norm[n_split:]\n",
+    "n_split = -int(len(dfs_norm)*0.2)\n",
+    "df_train = dfs_norm[:n_split]\n",
+    "df_test = dfs_norm[n_split:]\n",
     "\n",
     "# Show split\n",
-    "df_train['energy(kWh/hh)'].plot(label='train')\n",
-    "df_test['energy(kWh/hh)'].plot(label='test')\n",
+    "pd.concat(df_train)['energy(kWh/hh)'].plot(label='train')\n",
+    "pd.concat(df_test)['energy(kWh/hh)'].plot(label='test')\n",
     "plt.ylabel('energy(kWh/hh)')\n",
     "plt.legend()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:37.951025Z",
-     "start_time": "2020-10-18T07:17:37.888800Z"
+     "end_time": "2020-10-18T07:21:38.239847Z",
+     "start_time": "2020-10-18T07:21:37.709627Z"
     },
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy(kWh/hh)</th>\n",
-       "      <th>month</th>\n",
-       "      <th>day</th>\n",
-       "      <th>week</th>\n",
-       "      <th>hour</th>\n",
-       "      <th>minute</th>\n",
-       "      <th>dayofweek</th>\n",
-       "      <th>visibility</th>\n",
-       "      <th>windBearing</th>\n",
-       "      <th>temperature</th>\n",
-       "      <th>dewPoint</th>\n",
-       "      <th>pressure</th>\n",
-       "      <th>apparentTemperature</th>\n",
-       "      <th>windSpeed</th>\n",
-       "      <th>humidity</th>\n",
-       "      <th>holiday</th>\n",
-       "      <th>LCLid</th>\n",
-       "      <th>stdorToU</th>\n",
-       "      <th>Acorn_grouped</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2012-10-12 11:30:00</th>\n",
-       "      <td>-0.444130</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>-0.072904</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.800636</td>\n",
-       "      <td>0.453439</td>\n",
-       "      <td>-0.305918</td>\n",
-       "      <td>-0.458899</td>\n",
-       "      <td>0.577361</td>\n",
-       "      <td>1.406991</td>\n",
-       "      <td>-1.492695</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012-10-12 12:00:00</th>\n",
-       "      <td>1.660500</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>0.071557</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.724633</td>\n",
-       "      <td>0.667233</td>\n",
-       "      <td>-0.336717</td>\n",
-       "      <td>-0.448391</td>\n",
-       "      <td>0.754344</td>\n",
-       "      <td>1.387977</td>\n",
-       "      <td>-1.854084</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012-10-12 12:30:00</th>\n",
-       "      <td>0.013222</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>0.071557</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.724633</td>\n",
-       "      <td>0.667233</td>\n",
-       "      <td>-0.336717</td>\n",
-       "      <td>-0.448391</td>\n",
-       "      <td>0.754344</td>\n",
-       "      <td>1.387977</td>\n",
-       "      <td>-1.854084</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012-10-12 13:00:00</th>\n",
-       "      <td>-0.395562</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>0.216018</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.648631</td>\n",
-       "      <td>0.689117</td>\n",
-       "      <td>-0.305918</td>\n",
-       "      <td>-0.433505</td>\n",
-       "      <td>0.772460</td>\n",
-       "      <td>1.426005</td>\n",
-       "      <td>-1.854084</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012-10-12 13:30:00</th>\n",
-       "      <td>-0.217478</td>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>-0.445950</td>\n",
-       "      <td>0.822749</td>\n",
-       "      <td>0.216018</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.759528</td>\n",
-       "      <td>0.648631</td>\n",
-       "      <td>0.689117</td>\n",
-       "      <td>-0.305918</td>\n",
-       "      <td>-0.433505</td>\n",
-       "      <td>0.772460</td>\n",
-       "      <td>1.426005</td>\n",
-       "      <td>-1.854084</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-02-27 22:00:00</th>\n",
-       "      <td>0.660800</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.269408</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>1.516170</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.000435</td>\n",
-       "      <td>0.912217</td>\n",
-       "      <td>0.246907</td>\n",
-       "      <td>-0.975781</td>\n",
-       "      <td>-0.852602</td>\n",
-       "      <td>-0.597253</td>\n",
-       "      <td>-0.980649</td>\n",
-       "      <td>-0.465873</td>\n",
-       "      <td>0.386532</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-02-27 22:30:00</th>\n",
-       "      <td>4.441040</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.269408</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>1.516170</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.000435</td>\n",
-       "      <td>0.912217</td>\n",
-       "      <td>0.246907</td>\n",
-       "      <td>-0.975781</td>\n",
-       "      <td>-0.852602</td>\n",
-       "      <td>-0.597253</td>\n",
-       "      <td>-0.980649</td>\n",
-       "      <td>-0.465873</td>\n",
-       "      <td>0.386532</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-02-27 23:00:00</th>\n",
-       "      <td>4.024161</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.269408</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>1.660631</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.000435</td>\n",
-       "      <td>0.921760</td>\n",
-       "      <td>0.073188</td>\n",
-       "      <td>-1.004399</td>\n",
-       "      <td>-0.858377</td>\n",
-       "      <td>-0.689197</td>\n",
-       "      <td>-0.979256</td>\n",
-       "      <td>-0.594216</td>\n",
-       "      <td>0.458810</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-02-27 23:30:00</th>\n",
-       "      <td>3.906788</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.269408</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>1.660631</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.000435</td>\n",
-       "      <td>0.921760</td>\n",
-       "      <td>0.073188</td>\n",
-       "      <td>-1.004399</td>\n",
-       "      <td>-0.858377</td>\n",
-       "      <td>-0.689197</td>\n",
-       "      <td>-0.979256</td>\n",
-       "      <td>-0.594216</td>\n",
-       "      <td>0.458810</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2014-02-28 00:00:00</th>\n",
-       "      <td>4.590792</td>\n",
-       "      <td>-1.203661</td>\n",
-       "      <td>1.383766</td>\n",
-       "      <td>-1.069273</td>\n",
-       "      <td>-1.661979</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.499755</td>\n",
-       "      <td>0.476418</td>\n",
-       "      <td>-0.035387</td>\n",
-       "      <td>-1.024600</td>\n",
-       "      <td>-0.873777</td>\n",
-       "      <td>-0.781141</td>\n",
-       "      <td>-0.972288</td>\n",
-       "      <td>-0.698792</td>\n",
-       "      <td>0.458810</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>24170 rows × 19 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     energy(kWh/hh)     month       day      week      hour  \\\n",
-       "2012-10-12 11:30:00       -0.444130  0.857219 -0.445950  0.822749 -0.072904   \n",
-       "2012-10-12 12:00:00        1.660500  0.857219 -0.445950  0.822749  0.071557   \n",
-       "2012-10-12 12:30:00        0.013222  0.857219 -0.445950  0.822749  0.071557   \n",
-       "2012-10-12 13:00:00       -0.395562  0.857219 -0.445950  0.822749  0.216018   \n",
-       "2012-10-12 13:30:00       -0.217478  0.857219 -0.445950  0.822749  0.216018   \n",
-       "...                             ...       ...       ...       ...       ...   \n",
-       "2014-02-27 22:00:00        0.660800 -1.203661  1.269408 -1.069273  1.516170   \n",
-       "2014-02-27 22:30:00        4.441040 -1.203661  1.269408 -1.069273  1.516170   \n",
-       "2014-02-27 23:00:00        4.024161 -1.203661  1.269408 -1.069273  1.660631   \n",
-       "2014-02-27 23:30:00        3.906788 -1.203661  1.269408 -1.069273  1.660631   \n",
-       "2014-02-28 00:00:00        4.590792 -1.203661  1.383766 -1.069273 -1.661979   \n",
-       "\n",
-       "                       minute  dayofweek  visibility  windBearing  \\\n",
-       "2012-10-12 11:30:00  1.000041   0.499755    0.759528     0.800636   \n",
-       "2012-10-12 12:00:00 -0.999959   0.499755    0.759528     0.724633   \n",
-       "2012-10-12 12:30:00  1.000041   0.499755    0.759528     0.724633   \n",
-       "2012-10-12 13:00:00 -0.999959   0.499755    0.759528     0.648631   \n",
-       "2012-10-12 13:30:00  1.000041   0.499755    0.759528     0.648631   \n",
-       "...                       ...        ...         ...          ...   \n",
-       "2014-02-27 22:00:00 -0.999959   0.000435    0.912217     0.246907   \n",
-       "2014-02-27 22:30:00  1.000041   0.000435    0.912217     0.246907   \n",
-       "2014-02-27 23:00:00 -0.999959   0.000435    0.921760     0.073188   \n",
-       "2014-02-27 23:30:00  1.000041   0.000435    0.921760     0.073188   \n",
-       "2014-02-28 00:00:00 -0.999959   0.499755    0.476418    -0.035387   \n",
-       "\n",
-       "                     temperature  dewPoint  pressure  apparentTemperature  \\\n",
-       "2012-10-12 11:30:00     0.453439 -0.305918 -0.458899             0.577361   \n",
-       "2012-10-12 12:00:00     0.667233 -0.336717 -0.448391             0.754344   \n",
-       "2012-10-12 12:30:00     0.667233 -0.336717 -0.448391             0.754344   \n",
-       "2012-10-12 13:00:00     0.689117 -0.305918 -0.433505             0.772460   \n",
-       "2012-10-12 13:30:00     0.689117 -0.305918 -0.433505             0.772460   \n",
-       "...                          ...       ...       ...                  ...   \n",
-       "2014-02-27 22:00:00    -0.975781 -0.852602 -0.597253            -0.980649   \n",
-       "2014-02-27 22:30:00    -0.975781 -0.852602 -0.597253            -0.980649   \n",
-       "2014-02-27 23:00:00    -1.004399 -0.858377 -0.689197            -0.979256   \n",
-       "2014-02-27 23:30:00    -1.004399 -0.858377 -0.689197            -0.979256   \n",
-       "2014-02-28 00:00:00    -1.024600 -0.873777 -0.781141            -0.972288   \n",
-       "\n",
-       "                     windSpeed  humidity   holiday  LCLid  stdorToU  \\\n",
-       "2012-10-12 11:30:00   1.406991 -1.492695 -0.149604    0.0       0.0   \n",
-       "2012-10-12 12:00:00   1.387977 -1.854084 -0.149604    0.0       0.0   \n",
-       "2012-10-12 12:30:00   1.387977 -1.854084 -0.149604    0.0       0.0   \n",
-       "2012-10-12 13:00:00   1.426005 -1.854084 -0.149604    0.0       0.0   \n",
-       "2012-10-12 13:30:00   1.426005 -1.854084 -0.149604    0.0       0.0   \n",
-       "...                        ...       ...       ...    ...       ...   \n",
-       "2014-02-27 22:00:00  -0.465873  0.386532 -0.149604    0.0       0.0   \n",
-       "2014-02-27 22:30:00  -0.465873  0.386532 -0.149604    0.0       0.0   \n",
-       "2014-02-27 23:00:00  -0.594216  0.458810 -0.149604    0.0       0.0   \n",
-       "2014-02-27 23:30:00  -0.594216  0.458810 -0.149604    0.0       0.0   \n",
-       "2014-02-28 00:00:00  -0.698792  0.458810 -0.149604    0.0       0.0   \n",
-       "\n",
-       "                     Acorn_grouped  \n",
-       "2012-10-12 11:30:00            0.0  \n",
-       "2012-10-12 12:00:00            0.0  \n",
-       "2012-10-12 12:30:00            0.0  \n",
-       "2012-10-12 13:00:00            0.0  \n",
-       "2012-10-12 13:30:00            0.0  \n",
-       "...                            ...  \n",
-       "2014-02-27 22:00:00            0.0  \n",
-       "2014-02-27 22:30:00            0.0  \n",
-       "2014-02-27 23:00:00            0.0  \n",
-       "2014-02-27 23:30:00            0.0  \n",
-       "2014-02-28 00:00:00            0.0  \n",
-       "\n",
-       "[24170 rows x 19 columns]"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_norm"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -1421,11 +1082,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 32,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:37.988816Z",
-     "start_time": "2020-10-18T07:17:37.953983Z"
+     "end_time": "2020-10-18T07:28:37.972005Z",
+     "start_time": "2020-10-18T07:28:37.922547Z"
     }
    },
    "outputs": [],
@@ -1439,11 +1100,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 33,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:17:38.065288Z",
-     "start_time": "2020-10-18T07:17:37.991192Z"
+     "end_time": "2020-10-18T07:28:38.223683Z",
+     "start_time": "2020-10-18T07:28:38.086585Z"
     }
    },
    "outputs": [
@@ -1451,17 +1112,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<Seq2SeqDataSet(shape=(19347, 19), times=2012-10-12 11:30:00 to 2012-10-12 12:00:00 at 30T)>\n",
-      "<Seq2SeqDataSet(shape=(4823, 19), times=2013-11-19 13:00:00 to 2013-11-19 13:30:00 at 30T)>\n"
+      "<Seq2SeqDataSets([<Seq2SeqDataSet(shape=(24170, 19), times=2012-10-12 11:30:00 to 2012-10-12 12:00:00 at 30T)>, <Seq2SeqDataSet(shape=(39247, 19), times=2011-12-03 09:00:00 to 2011-12-03 09:30:00 at 30T)>, <Seq2SeqDataSet(shape=(20082, 19), times=2012-03-22 15:30:00 to 2012-03-22 16:00:00 at 30T)>, <Seq2SeqDataSet(shape=(10875, 19), times=2012-05-08 10:30:00 to 2012-05-08 11:00:00 at 30T)>, <Seq2SeqDataSet(shape=(25368, 19), times=2012-09-17 12:30:00 to 2012-09-17 13:00:00 at 30T)>])>\n",
+      "<Seq2SeqDataSets([<Seq2SeqDataSet(shape=(25327, 19), times=2012-09-18 09:00:00 to 2012-09-18 09:30:00 at 30T)>])>\n"
      ]
     }
    ],
    "source": [
-    "ds_train = Seq2SeqDataSet(df_train,\n",
+    "ds_train = Seq2SeqDataSets(df_train,\n",
     "                          window_past=window_past,\n",
     "                          window_future=window_future,\n",
     "                          columns_blank=columns_blank)\n",
-    "ds_test = Seq2SeqDataSet(df_test,\n",
+    "ds_test = Seq2SeqDataSets(df_test,\n",
     "                         window_past=window_past,\n",
     "                         window_future=window_future,\n",
     "                         columns_blank=columns_blank)\n",
@@ -1471,430 +1132,429 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 34,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:35.927144Z",
-     "start_time": "2020-10-18T06:50:35.880239Z"
+     "end_time": "2020-10-18T07:28:38.291525Z",
+     "start_time": "2020-10-18T07:28:38.244447Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[array([[ 0.85721916, -0.44595006,  0.8227485 , ...,  0.        ,\n",
+       "[array([[-1.2801421 ,  0.4781144 , -1.2036566 , ...,  1.        ,\n",
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-       "        [-0.72744596],\n",
-       "        [ 0.04560087],\n",
-       "        [ 1.021016  ]], dtype=float32)]"
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+       "          3.9375    ,  0.        ],\n",
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+       "          3.9583333 ,  0.        ]], dtype=float32),\n",
+       " array([[-6.32189587e-02],\n",
+       "        [-9.11520571e-02],\n",
+       "        [-5.47940433e-01],\n",
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+       "        [-4.13204283e-01],\n",
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+       "        [ 5.01565747e-02],\n",
+       "        [-3.77055556e-01],\n",
+       "        [-2.43962556e-01],\n",
+       "        [-7.14345723e-02],\n",
+       "        [-8.45795646e-02],\n",
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+       "        [ 4.19409573e-02],\n",
+       "        [-5.00739664e-02],\n",
+       "        [ 3.37253399e-02],\n",
+       "        [-6.48620799e-02],\n",
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+       "        [-6.64602220e-01],\n",
+       "        [-5.77516675e-01],\n",
+       "        [-6.41598463e-01],\n",
+       "        [-6.02163494e-01],\n",
+       "        [-6.15308523e-01],\n",
+       "        [-6.31739736e-01],\n",
+       "        [-5.89018524e-01],\n",
+       "        [-6.61315978e-01],\n",
+       "        [-5.87375402e-01],\n",
+       "        [-6.56386614e-01],\n",
+       "        [-5.47940433e-01],\n",
+       "        [-5.85732281e-01],\n",
+       "        [-7.80070648e-02],\n",
+       "        [-3.75412434e-01],\n",
+       "        [-2.58750677e-01],\n",
+       "        [-1.99598223e-01],\n",
+       "        [-2.42319435e-01],\n",
+       "        [ 2.83480138e-01],\n",
+       "        [-5.72587252e-01],\n",
+       "        [-5.84089160e-01],\n",
+       "        [-6.03806615e-01],\n",
+       "        [-6.05449736e-01],\n",
+       "        [-6.43241584e-01],\n",
+       "        [-5.33152342e-01],\n",
+       "        [-6.59672856e-01],\n",
+       "        [-5.87375402e-01],\n",
+       "        [-5.74230433e-01],\n",
+       "        [-6.25167251e-01],\n",
+       "        [-5.51226676e-01],\n",
+       "        [-4.06631798e-01],\n",
+       "        [-2.71895647e-01],\n",
+       "        [-5.70962811e-03],\n",
+       "        [ 8.99651468e-01],\n",
+       "        [ 2.16112062e-01],\n",
+       "        [-3.86914313e-01],\n",
+       "        [-3.95129919e-01],\n",
+       "        [ 6.33015633e-02],\n",
+       "        [-5.50033376e-02],\n",
+       "        [-2.06170708e-01],\n",
+       "        [-2.86683768e-01],\n",
+       "        [-2.78468162e-01],\n",
+       "        [ 1.14940608e+00],\n",
+       "        [ 1.10339880e+00],\n",
+       "        [ 8.22424710e-01],\n",
+       "        [-2.06170708e-01],\n",
+       "        [-4.64141130e-01],\n",
+       "        [-5.57799160e-01],\n",
+       "        [-6.28453493e-01],\n",
+       "        [-5.97234130e-01],\n",
+       "        [-6.16951644e-01],\n",
+       "        [-6.15308523e-01],\n",
+       "        [-6.07092857e-01],\n",
+       "        [-6.30096614e-01],\n",
+       "        [-5.93947887e-01],\n",
+       "        [-6.43241584e-01],\n",
+       "        [-5.87375402e-01],\n",
+       "        [-6.54743433e-01],\n",
+       "        [-5.52869797e-01],\n",
+       "        [ 2.27613926e-01],\n",
+       "        [ 1.83249593e-01],\n",
+       "        [ 9.28777903e-02],\n",
+       "        [ 1.86535835e-01],\n",
+       "        [ 5.16803682e-01],\n",
+       "        [-3.98416162e-01],\n",
+       "        [-3.01471889e-01],\n",
+       "        [-5.93947887e-01],\n",
+       "        [ 1.55316487e-01],\n",
+       "        [-6.25167251e-01],\n",
+       "        [-5.72587252e-01],\n",
+       "        [-6.56386614e-01],\n",
+       "        [-5.72587252e-01],\n",
+       "        [-6.58029735e-01],\n",
+       "        [-5.72587252e-01],\n",
+       "        [-6.48170948e-01],\n",
+       "        [-5.82446039e-01],\n",
+       "        [-6.33382857e-01],\n",
+       "        [-5.70944130e-01],\n",
+       "        [-3.68839949e-01],\n",
+       "        [-1.14155784e-01],\n",
+       "        [ 2.35829547e-01],\n",
+       "        [ 9.78521407e-01],\n",
+       "        [ 1.34000862e+00],\n",
+       "        [ 7.92848468e-01],\n",
+       "        [ 6.00153171e-02],\n",
+       "        [ 4.14911378e-03],\n",
+       "        [-1.05940171e-01],\n",
+       "        [ 1.42873728e+00],\n",
+       "        [ 1.25949562e+00],\n",
+       "        [ 1.21184516e+00],\n",
+       "        [ 1.48953283e+00]], dtype=float32)]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1903,16 +1563,16 @@
     "# we can treat it like an array\n",
     "ds_train[0]\n",
     "len(ds_train)\n",
-    "ds_train[0]"
+    "ds_train[-1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 35,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:36.307909Z",
-     "start_time": "2020-10-18T06:50:35.929008Z"
+     "end_time": "2020-10-18T07:28:38.848911Z",
+     "start_time": "2020-10-18T07:28:38.405411Z"
     }
    },
    "outputs": [
@@ -1952,8 +1612,8 @@
        "      <th>windSpeed</th>\n",
        "      <th>humidity</th>\n",
        "      <th>holiday</th>\n",
-       "      <th>LCLid</th>\n",
        "      <th>stdorToU</th>\n",
+       "      <th>LCLid</th>\n",
        "      <th>Acorn_grouped</th>\n",
        "      <th>tsp_days</th>\n",
        "      <th>is_past</th>\n",
@@ -1961,117 +1621,117 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2012-10-16 14:00:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.011479</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.360479</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>-0.998205</td>\n",
-       "      <td>0.540038</td>\n",
-       "      <td>0.529200</td>\n",
-       "      <td>0.815373</td>\n",
-       "      <td>-0.344417</td>\n",
-       "      <td>-0.843313</td>\n",
-       "      <td>0.876978</td>\n",
-       "      <td>2.267368</td>\n",
-       "      <td>-2.070918</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2014-02-23 17:00:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>0.819931</td>\n",
+       "      <td>-1.203657</td>\n",
+       "      <td>0.793780</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>1.501256</td>\n",
+       "      <td>0.297982</td>\n",
+       "      <td>0.108245</td>\n",
+       "      <td>0.168823</td>\n",
+       "      <td>-0.056013</td>\n",
+       "      <td>-0.272611</td>\n",
+       "      <td>0.320145</td>\n",
+       "      <td>2.045089</td>\n",
+       "      <td>-0.593609</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-0.104167</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-16 14:30:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.011479</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.360479</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>-0.998205</td>\n",
-       "      <td>0.540038</td>\n",
-       "      <td>0.529200</td>\n",
-       "      <td>0.815373</td>\n",
-       "      <td>-0.344417</td>\n",
-       "      <td>-0.843313</td>\n",
-       "      <td>0.876978</td>\n",
-       "      <td>2.267368</td>\n",
-       "      <td>-2.070918</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2014-02-23 17:30:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>0.819931</td>\n",
+       "      <td>-1.203657</td>\n",
+       "      <td>0.793780</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>1.501256</td>\n",
+       "      <td>0.297982</td>\n",
+       "      <td>0.108245</td>\n",
+       "      <td>0.168823</td>\n",
+       "      <td>-0.056013</td>\n",
+       "      <td>-0.272611</td>\n",
+       "      <td>0.320145</td>\n",
+       "      <td>2.045089</td>\n",
+       "      <td>-0.593609</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-0.083333</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-16 15:00:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.011479</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.504941</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>-0.998205</td>\n",
-       "      <td>0.600477</td>\n",
-       "      <td>0.518342</td>\n",
-       "      <td>0.798539</td>\n",
-       "      <td>-0.261644</td>\n",
-       "      <td>-0.818795</td>\n",
-       "      <td>0.863042</td>\n",
-       "      <td>1.753994</td>\n",
-       "      <td>-1.998640</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2014-02-23 18:00:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>0.819931</td>\n",
+       "      <td>-1.203657</td>\n",
+       "      <td>0.938242</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>1.501256</td>\n",
+       "      <td>0.297982</td>\n",
+       "      <td>0.020721</td>\n",
+       "      <td>0.098036</td>\n",
+       "      <td>-0.096592</td>\n",
+       "      <td>-0.240999</td>\n",
+       "      <td>0.261351</td>\n",
+       "      <td>1.902797</td>\n",
+       "      <td>-0.522607</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-0.062500</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-16 15:30:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.011479</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.504941</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>-0.998205</td>\n",
-       "      <td>0.600477</td>\n",
-       "      <td>0.518342</td>\n",
-       "      <td>0.798539</td>\n",
-       "      <td>-0.261644</td>\n",
-       "      <td>-0.818795</td>\n",
-       "      <td>0.863042</td>\n",
-       "      <td>1.753994</td>\n",
-       "      <td>-1.998640</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2014-02-23 18:30:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>0.819931</td>\n",
+       "      <td>-1.203657</td>\n",
+       "      <td>0.938242</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>1.501256</td>\n",
+       "      <td>0.297982</td>\n",
+       "      <td>0.020721</td>\n",
+       "      <td>0.098036</td>\n",
+       "      <td>-0.096592</td>\n",
+       "      <td>-0.240999</td>\n",
+       "      <td>0.261351</td>\n",
+       "      <td>1.902797</td>\n",
+       "      <td>-0.522607</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-0.041667</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-16 16:00:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.011479</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.649402</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>-0.998205</td>\n",
-       "      <td>0.492323</td>\n",
-       "      <td>0.485770</td>\n",
-       "      <td>0.635248</td>\n",
-       "      <td>-0.232770</td>\n",
-       "      <td>-0.805660</td>\n",
-       "      <td>0.727866</td>\n",
-       "      <td>0.741507</td>\n",
-       "      <td>-1.709528</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2014-02-23 19:00:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>0.819931</td>\n",
+       "      <td>-1.203657</td>\n",
+       "      <td>1.082703</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>1.501256</td>\n",
+       "      <td>0.590561</td>\n",
+       "      <td>-0.077744</td>\n",
+       "      <td>0.059272</td>\n",
+       "      <td>-0.092727</td>\n",
+       "      <td>-0.216613</td>\n",
+       "      <td>0.229155</td>\n",
+       "      <td>1.201150</td>\n",
+       "      <td>-0.451605</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>-0.020833</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -2081,48 +1741,48 @@
       ],
       "text/plain": [
        "                        month       day      week      hour    minute  \\\n",
-       "2012-10-16 14:00:00  0.857219  0.011479  0.881874  0.360479 -0.999959   \n",
-       "2012-10-16 14:30:00  0.857219  0.011479  0.881874  0.360479  1.000041   \n",
-       "2012-10-16 15:00:00  0.857219  0.011479  0.881874  0.504941 -0.999959   \n",
-       "2012-10-16 15:30:00  0.857219  0.011479  0.881874  0.504941  1.000041   \n",
-       "2012-10-16 16:00:00  0.857219  0.011479  0.881874  0.649402 -0.999959   \n",
+       "2014-02-23 17:00:00 -1.280142  0.819931 -1.203657  0.793780 -0.999965   \n",
+       "2014-02-23 17:30:00 -1.280142  0.819931 -1.203657  0.793780  1.000035   \n",
+       "2014-02-23 18:00:00 -1.280142  0.819931 -1.203657  0.938242 -0.999965   \n",
+       "2014-02-23 18:30:00 -1.280142  0.819931 -1.203657  0.938242  1.000035   \n",
+       "2014-02-23 19:00:00 -1.280142  0.819931 -1.203657  1.082703 -0.999965   \n",
        "\n",
        "                     dayofweek  visibility  windBearing  temperature  \\\n",
-       "2012-10-16 14:00:00  -0.998205    0.540038     0.529200     0.815373   \n",
-       "2012-10-16 14:30:00  -0.998205    0.540038     0.529200     0.815373   \n",
-       "2012-10-16 15:00:00  -0.998205    0.600477     0.518342     0.798539   \n",
-       "2012-10-16 15:30:00  -0.998205    0.600477     0.518342     0.798539   \n",
-       "2012-10-16 16:00:00  -0.998205    0.492323     0.485770     0.635248   \n",
+       "2014-02-23 17:00:00   1.501256    0.297982     0.108245     0.168823   \n",
+       "2014-02-23 17:30:00   1.501256    0.297982     0.108245     0.168823   \n",
+       "2014-02-23 18:00:00   1.501256    0.297982     0.020721     0.098036   \n",
+       "2014-02-23 18:30:00   1.501256    0.297982     0.020721     0.098036   \n",
+       "2014-02-23 19:00:00   1.501256    0.590561    -0.077744     0.059272   \n",
        "\n",
        "                     dewPoint  pressure  apparentTemperature  windSpeed  \\\n",
-       "2012-10-16 14:00:00 -0.344417 -0.843313             0.876978   2.267368   \n",
-       "2012-10-16 14:30:00 -0.344417 -0.843313             0.876978   2.267368   \n",
-       "2012-10-16 15:00:00 -0.261644 -0.818795             0.863042   1.753994   \n",
-       "2012-10-16 15:30:00 -0.261644 -0.818795             0.863042   1.753994   \n",
-       "2012-10-16 16:00:00 -0.232770 -0.805660             0.727866   0.741507   \n",
+       "2014-02-23 17:00:00 -0.056013 -0.272611             0.320145   2.045089   \n",
+       "2014-02-23 17:30:00 -0.056013 -0.272611             0.320145   2.045089   \n",
+       "2014-02-23 18:00:00 -0.096592 -0.240999             0.261351   1.902797   \n",
+       "2014-02-23 18:30:00 -0.096592 -0.240999             0.261351   1.902797   \n",
+       "2014-02-23 19:00:00 -0.092727 -0.216613             0.229155   1.201150   \n",
        "\n",
-       "                     humidity   holiday  LCLid  stdorToU  Acorn_grouped  \\\n",
-       "2012-10-16 14:00:00 -2.070918 -0.149604    0.0       0.0            0.0   \n",
-       "2012-10-16 14:30:00 -2.070918 -0.149604    0.0       0.0            0.0   \n",
-       "2012-10-16 15:00:00 -1.998640 -0.149604    0.0       0.0            0.0   \n",
-       "2012-10-16 15:30:00 -1.998640 -0.149604    0.0       0.0            0.0   \n",
-       "2012-10-16 16:00:00 -1.709528 -0.149604    0.0       0.0            0.0   \n",
+       "                     humidity   holiday  stdorToU  LCLid  Acorn_grouped  \\\n",
+       "2014-02-23 17:00:00 -0.593609 -0.149527       0.0    0.0            1.0   \n",
+       "2014-02-23 17:30:00 -0.593609 -0.149527       0.0    0.0            1.0   \n",
+       "2014-02-23 18:00:00 -0.522607 -0.149527       0.0    0.0            1.0   \n",
+       "2014-02-23 18:30:00 -0.522607 -0.149527       0.0    0.0            1.0   \n",
+       "2014-02-23 19:00:00 -0.451605 -0.149527       0.0    0.0            1.0   \n",
        "\n",
        "                     tsp_days  is_past  \n",
-       "2012-10-16 14:00:00 -0.104167      1.0  \n",
-       "2012-10-16 14:30:00 -0.083333      1.0  \n",
-       "2012-10-16 15:00:00 -0.062500      1.0  \n",
-       "2012-10-16 15:30:00 -0.041667      1.0  \n",
-       "2012-10-16 16:00:00 -0.020833      1.0  "
+       "2014-02-23 17:00:00 -0.104167      1.0  \n",
+       "2014-02-23 17:30:00 -0.083333      1.0  \n",
+       "2014-02-23 18:00:00 -0.062500      1.0  \n",
+       "2014-02-23 18:30:00 -0.041667      1.0  \n",
+       "2014-02-23 19:00:00 -0.020833      1.0  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2149,11 +1809,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 36,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:36.368746Z",
-     "start_time": "2020-10-18T06:50:36.309745Z"
+     "end_time": "2020-10-18T07:28:38.930034Z",
+     "start_time": "2020-10-18T07:28:38.851766Z"
     }
    },
    "outputs": [
@@ -2193,8 +1853,8 @@
        "      <th>windSpeed</th>\n",
        "      <th>humidity</th>\n",
        "      <th>holiday</th>\n",
-       "      <th>LCLid</th>\n",
        "      <th>stdorToU</th>\n",
+       "      <th>LCLid</th>\n",
        "      <th>Acorn_grouped</th>\n",
        "      <th>tsp_days</th>\n",
        "      <th>is_past</th>\n",
@@ -2202,13 +1862,13 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2012-10-20 14:00:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.468908</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.360479</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.999075</td>\n",
+       "      <th>2014-02-27 17:00:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>0.793780</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>0.001332</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2217,21 +1877,21 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>3.895833</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-20 14:30:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.468908</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.360479</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.999075</td>\n",
+       "      <th>2014-02-27 17:30:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>0.793780</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>0.001332</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2240,21 +1900,21 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>3.916667</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-20 15:00:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.468908</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.504941</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.999075</td>\n",
+       "      <th>2014-02-27 18:00:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>0.938242</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>0.001332</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2263,21 +1923,21 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>3.937500</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-20 15:30:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.468908</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.504941</td>\n",
-       "      <td>1.000041</td>\n",
-       "      <td>0.999075</td>\n",
+       "      <th>2014-02-27 18:30:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>0.938242</td>\n",
+       "      <td>1.000035</td>\n",
+       "      <td>0.001332</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2286,21 +1946,21 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>3.958333</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2012-10-20 16:00:00</th>\n",
-       "      <td>0.857219</td>\n",
-       "      <td>0.468908</td>\n",
-       "      <td>0.881874</td>\n",
-       "      <td>0.649402</td>\n",
-       "      <td>-0.999959</td>\n",
-       "      <td>0.999075</td>\n",
+       "      <th>2014-02-27 19:00:00</th>\n",
+       "      <td>-1.280142</td>\n",
+       "      <td>1.275686</td>\n",
+       "      <td>-1.141131</td>\n",
+       "      <td>1.082703</td>\n",
+       "      <td>-0.999965</td>\n",
+       "      <td>0.001332</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2309,10 +1969,10 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.149604</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.149527</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>3.979167</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -2322,42 +1982,42 @@
       ],
       "text/plain": [
        "                        month       day      week      hour    minute  \\\n",
-       "2012-10-20 14:00:00  0.857219  0.468908  0.881874  0.360479 -0.999959   \n",
-       "2012-10-20 14:30:00  0.857219  0.468908  0.881874  0.360479  1.000041   \n",
-       "2012-10-20 15:00:00  0.857219  0.468908  0.881874  0.504941 -0.999959   \n",
-       "2012-10-20 15:30:00  0.857219  0.468908  0.881874  0.504941  1.000041   \n",
-       "2012-10-20 16:00:00  0.857219  0.468908  0.881874  0.649402 -0.999959   \n",
+       "2014-02-27 17:00:00 -1.280142  1.275686 -1.141131  0.793780 -0.999965   \n",
+       "2014-02-27 17:30:00 -1.280142  1.275686 -1.141131  0.793780  1.000035   \n",
+       "2014-02-27 18:00:00 -1.280142  1.275686 -1.141131  0.938242 -0.999965   \n",
+       "2014-02-27 18:30:00 -1.280142  1.275686 -1.141131  0.938242  1.000035   \n",
+       "2014-02-27 19:00:00 -1.280142  1.275686 -1.141131  1.082703 -0.999965   \n",
        "\n",
        "                     dayofweek  visibility  windBearing  temperature  \\\n",
-       "2012-10-20 14:00:00   0.999075         0.0          0.0          0.0   \n",
-       "2012-10-20 14:30:00   0.999075         0.0          0.0          0.0   \n",
-       "2012-10-20 15:00:00   0.999075         0.0          0.0          0.0   \n",
-       "2012-10-20 15:30:00   0.999075         0.0          0.0          0.0   \n",
-       "2012-10-20 16:00:00   0.999075         0.0          0.0          0.0   \n",
+       "2014-02-27 17:00:00   0.001332         0.0          0.0          0.0   \n",
+       "2014-02-27 17:30:00   0.001332         0.0          0.0          0.0   \n",
+       "2014-02-27 18:00:00   0.001332         0.0          0.0          0.0   \n",
+       "2014-02-27 18:30:00   0.001332         0.0          0.0          0.0   \n",
+       "2014-02-27 19:00:00   0.001332         0.0          0.0          0.0   \n",
        "\n",
        "                     dewPoint  pressure  apparentTemperature  windSpeed  \\\n",
-       "2012-10-20 14:00:00       0.0       0.0                  0.0        0.0   \n",
-       "2012-10-20 14:30:00       0.0       0.0                  0.0        0.0   \n",
-       "2012-10-20 15:00:00       0.0       0.0                  0.0        0.0   \n",
-       "2012-10-20 15:30:00       0.0       0.0                  0.0        0.0   \n",
-       "2012-10-20 16:00:00       0.0       0.0                  0.0        0.0   \n",
+       "2014-02-27 17:00:00       0.0       0.0                  0.0        0.0   \n",
+       "2014-02-27 17:30:00       0.0       0.0                  0.0        0.0   \n",
+       "2014-02-27 18:00:00       0.0       0.0                  0.0        0.0   \n",
+       "2014-02-27 18:30:00       0.0       0.0                  0.0        0.0   \n",
+       "2014-02-27 19:00:00       0.0       0.0                  0.0        0.0   \n",
        "\n",
-       "                     humidity   holiday  LCLid  stdorToU  Acorn_grouped  \\\n",
-       "2012-10-20 14:00:00       0.0 -0.149604    0.0       0.0            0.0   \n",
-       "2012-10-20 14:30:00       0.0 -0.149604    0.0       0.0            0.0   \n",
-       "2012-10-20 15:00:00       0.0 -0.149604    0.0       0.0            0.0   \n",
-       "2012-10-20 15:30:00       0.0 -0.149604    0.0       0.0            0.0   \n",
-       "2012-10-20 16:00:00       0.0 -0.149604    0.0       0.0            0.0   \n",
+       "                     humidity   holiday  stdorToU  LCLid  Acorn_grouped  \\\n",
+       "2014-02-27 17:00:00       0.0 -0.149527       0.0    0.0            1.0   \n",
+       "2014-02-27 17:30:00       0.0 -0.149527       0.0    0.0            1.0   \n",
+       "2014-02-27 18:00:00       0.0 -0.149527       0.0    0.0            1.0   \n",
+       "2014-02-27 18:30:00       0.0 -0.149527       0.0    0.0            1.0   \n",
+       "2014-02-27 19:00:00       0.0 -0.149527       0.0    0.0            1.0   \n",
        "\n",
        "                     tsp_days  is_past  \n",
-       "2012-10-20 14:00:00  3.895833      0.0  \n",
-       "2012-10-20 14:30:00  3.916667      0.0  \n",
-       "2012-10-20 15:00:00  3.937500      0.0  \n",
-       "2012-10-20 15:30:00  3.958333      0.0  \n",
-       "2012-10-20 16:00:00  3.979167      0.0  "
+       "2014-02-27 17:00:00  3.895833      0.0  \n",
+       "2014-02-27 17:30:00  3.916667      0.0  \n",
+       "2014-02-27 18:00:00  3.937500      0.0  \n",
+       "2014-02-27 18:30:00  3.958333      0.0  \n",
+       "2014-02-27 19:00:00  3.979167      0.0  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2376,11 +2036,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 37,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:36.412045Z",
-     "start_time": "2020-10-18T06:50:36.370476Z"
+     "end_time": "2020-10-18T07:28:38.992920Z",
+     "start_time": "2020-10-18T07:28:38.933297Z"
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     "lines_to_next_cell": 0
@@ -2436,11 +2096,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 38,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T06:50:36.456344Z",
-     "start_time": "2020-10-18T06:50:36.414657Z"
+     "end_time": "2020-10-18T07:28:39.258866Z",
+     "start_time": "2020-10-18T07:28:39.199738Z"
     }
    },
    "outputs": [],
@@ -2472,16 +2132,16 @@
     "        return loss\n",
     "\n",
     "    def configure_optimizers(self):\n",
-    "        return torch.optim.Adam(self.parameters(), lr=1e-4)\n"
+    "        return torch.optim.Adam(self.parameters(), lr=1e-4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 39,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:06:30.808491Z",
-     "start_time": "2020-10-18T07:06:30.636543Z"
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@@ -2494,11 +2154,11 @@
   },
   {
    "cell_type": "code",
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+   "execution_count": 40,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:14:54.474098Z",
-     "start_time": "2020-10-18T07:13:40.188426Z"
+     "end_time": "2020-10-18T07:29:53.561973Z",
+     "start_time": "2020-10-18T07:28:41.332815Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2507,15 +2167,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/torch/nn/modules/rnn.py:60: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1\n",
-      "  \"num_layers={}\".format(dropout, num_layers))\n",
       "GPU available: True, used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
       "\n",
       "  | Name   | Type               | Params\n",
       "----------------------------------------------\n",
-      "0 | _model | Seq2SeqLSTMDecoder | 4 K   \n"
+      "0 | _model | Seq2SeqLSTMDecoder | 30 K  \n"
      ]
     },
     {
@@ -2535,7 +2193,7 @@
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        "version_major": 2,
        "version_minor": 0
       },
@@ -2546,62 +2204,6 @@
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-    },
-    {
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-    },
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-       "HBox(children=(HTML(value='Validating'), FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), m…"
-      ]
-     },
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-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -2623,7 +2225,7 @@
        "1"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2635,9 +2237,9 @@
     "model = PL_Seq2Seq(input_size=input_size,\n",
     "                   input_size_decoder=input_size,\n",
     "                   output_size=output_size,\n",
-    "                   hidden_size=16,\n",
-    "                   lstm_layers=1,\n",
-    "                   lstm_dropout=0.5).to(device)\n",
+    "                   hidden_size=32,\n",
+    "                   lstm_layers=2,\n",
+    "                   lstm_dropout=0.25).to(device)\n",
     "\n",
     "logger = CSVLogger(\"logs\", name=\"seq2seq\")\n",
     "trainer = pl.Trainer(gpus=1,\n",
@@ -2645,113 +2247,34 @@
     "dl_train = DataLoader(ds_train,\n",
     "                      batch_size=batch_size,\n",
     "                      shuffle=True,\n",
-    "                      num_workers=4)\n",
+    "                      num_workers=8)\n",
     "dl_test = DataLoader(ds_test, batch_size=batch_size, num_workers=4)\n",
     "trainer.fit(model, dl_train, dl_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 41,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:14:54.912034Z",
-     "start_time": "2020-10-18T07:14:54.476393Z"
+     "end_time": "2020-10-18T07:29:53.629832Z",
+     "start_time": "2020-10-18T07:29:53.564330Z"
     }
    },
    "outputs": [
     {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>loss/train</th>\n",
-       "      <th>step</th>\n",
-       "      <th>loss/val</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>epoch</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0.0</th>\n",
-       "      <td>1.017363</td>\n",
-       "      <td>173.500000</td>\n",
-       "      <td>1.072045</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1.0</th>\n",
-       "      <td>0.852601</td>\n",
-       "      <td>448.142857</td>\n",
-       "      <td>1.047129</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2.0</th>\n",
-       "      <td>0.862261</td>\n",
-       "      <td>747.714286</td>\n",
-       "      <td>1.061411</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3.0</th>\n",
-       "      <td>0.802863</td>\n",
-       "      <td>1047.285714</td>\n",
-       "      <td>1.078880</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4.0</th>\n",
-       "      <td>0.766091</td>\n",
-       "      <td>1299.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       loss/train         step  loss/val\n",
-       "epoch                                   \n",
-       "0.0      1.017363   173.500000  1.072045\n",
-       "1.0      0.852601   448.142857  1.047129\n",
-       "2.0      0.862261   747.714286  1.061411\n",
-       "3.0      0.802863  1047.285714  1.078880\n",
-       "4.0      0.766091  1299.000000       NaN"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "KeyError",
+     "evalue": "\"['loss/val'] not in index\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-41-c73e88407fa7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf_hist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'epoch'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_hist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'epoch'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mffill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf_histe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_hist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'epoch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'epoch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf_histe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss/train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'loss/val'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mdf_histe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2906\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2907\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2908\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2909\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2910\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1252\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1255\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1302\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1303\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1304\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1306\u001b[0m             \u001b[0;31m# we skip the warning on Categorical\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: \"['loss/val'] not in index\""
+     ]
     }
    ],
    "source": [
@@ -2771,23 +2294,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 43,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:14:56.985869Z",
-     "start_time": "2020-10-18T07:14:54.915773Z"
+     "end_time": "2020-10-18T07:30:35.288889Z",
+     "start_time": "2020-10-18T07:30:19.895439Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "67bb6304e30f4fe692fcbb46fe21c55c",
+       "model_id": "a1a27b53e8ee46c89dfc751782392132",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=70.0), HTML(value='')))"
+       "HBox(children=(HTML(value='predict'), FloatProgress(value=0.0, max=390.0), HTML(value='')))"
       ]
      },
      "metadata": {},
@@ -3154,23 +2677,23 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:        (t_ahead: 192, t_behind: 192, t_source: 4439)\n",
+       "Dimensions:        (t_ahead: 192, t_behind: 192, t_source: 24943)\n",
        "Coordinates:\n",
-       "  * t_source       (t_source) datetime64[ns] 2013-11-19T13:00:00 ... 2013-11-...\n",
+       "  * t_source       (t_source) datetime64[ns] 2012-09-18T09:00:00 ... 2012-09-...\n",
        "  * t_ahead        (t_ahead) timedelta64[ns] 00:00:00 ... 3 days 23:30:00\n",
        "  * t_behind       (t_behind) timedelta64[ns] -4 days +00:00:00 ... -1 days +...\n",
-       "    t_target       (t_source, t_ahead) datetime64[ns] 2013-11-19T13:00:00 ......\n",
-       "    t_past         (t_source, t_behind) datetime64[ns] 2013-11-15T13:00:00 .....\n",
+       "    t_target       (t_source, t_ahead) datetime64[ns] 2012-09-18T09:00:00 ......\n",
+       "    t_past         (t_source, t_behind) datetime64[ns] 2012-09-14T09:00:00 .....\n",
        "    t_ahead_hours  (t_ahead) float64 0.0 0.0 1.0 1.0 2.0 ... 94.0 94.0 95.0 95.0\n",
        "Data variables:\n",
-       "    y_past         (t_source, t_behind) float32 0.44799998 0.318 ... 0.454 0.505\n",
-       "    nll            (t_source, t_ahead) float32 1.4400969 1.3171842 ... 3.8363848\n",
-       "    y_pred         (t_source, t_ahead) float32 0.3194818 ... 0.39490056\n",
-       "    y_pred_std     (t_source, t_ahead) float64 0.2927 0.2852 ... 0.4196 0.3664\n",
-       "    y_true         (t_source, t_ahead) float32 0.074000016 0.118 ... 1.2180002</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ebc3360e-5b54-4dde-8fa6-6bdcfb80ab33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebc3360e-5b54-4dde-8fa6-6bdcfb80ab33' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t_ahead</span>: 192</li><li><span class='xr-has-index'>t_behind</span>: 192</li><li><span class='xr-has-index'>t_source</span>: 4439</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ad0907d8-46e9-4357-b52c-52df0600c2de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad0907d8-46e9-4357-b52c-52df0600c2de' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_source</span></div><div class='xr-var-dims'>(t_source)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-11-19T13:00:00 ... 2013-11-...</div><input id='attrs-3515da9d-7b49-4184-87b6-d7bb02daf23f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3515da9d-7b49-4184-87b6-d7bb02daf23f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dae828fc-aef4-415c-8054-fd636c8e06cc' class='xr-var-data-in' type='checkbox'><label for='data-dae828fc-aef4-415c-8054-fd636c8e06cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-11-19T13:00:00.000000000&#x27;, &#x27;2013-11-19T13:30:00.000000000&#x27;,\n",
-       "       &#x27;2013-11-19T14:00:00.000000000&#x27;, ..., &#x27;2013-11-21T09:30:00.000000000&#x27;,\n",
-       "       &#x27;2013-11-21T10:00:00.000000000&#x27;, &#x27;2013-11-21T10:30:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_ahead</span></div><div class='xr-var-dims'>(t_ahead)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 ... 3 days 23:30:00</div><input id='attrs-738f43e7-6643-4e09-9b96-e06d8856af0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-738f43e7-6643-4e09-9b96-e06d8856af0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36732b5e-f3cb-49d7-8ebf-5595ba26b53e' class='xr-var-data-in' type='checkbox'><label for='data-36732b5e-f3cb-49d7-8ebf-5595ba26b53e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([              0,   1800000000000,   3600000000000,   5400000000000,\n",
+       "    y_past         (t_source, t_behind) float32 0.061000023 ... 0.12699997\n",
+       "    nll            (t_source, t_ahead) float32 0.44390392 ... 0.79414505\n",
+       "    y_pred         (t_source, t_ahead) float32 0.33915406 ... 0.37555674\n",
+       "    y_pred_std     (t_source, t_ahead) float64 0.3371 0.332 ... 0.3789 0.379\n",
+       "    y_true         (t_source, t_ahead) float32 0.17700002 ... 0.05900002</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8b4d7c9b-71b8-4f05-bcd9-5c11ad81eaee' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b4d7c9b-71b8-4f05-bcd9-5c11ad81eaee' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t_ahead</span>: 192</li><li><span class='xr-has-index'>t_behind</span>: 192</li><li><span class='xr-has-index'>t_source</span>: 24943</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-09977815-fd7c-4508-9a37-a3c367a1c6cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-09977815-fd7c-4508-9a37-a3c367a1c6cd' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_source</span></div><div class='xr-var-dims'>(t_source)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-09-18T09:00:00 ... 2012-09-...</div><input id='attrs-a20eeae5-9100-421e-bc3e-38248b208f86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a20eeae5-9100-421e-bc3e-38248b208f86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc21daf-cd77-4855-9db5-6b333c29ad4c' class='xr-var-data-in' type='checkbox'><label for='data-3dc21daf-cd77-4855-9db5-6b333c29ad4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-09-18T09:00:00.000000000&#x27;, &#x27;2012-09-18T09:30:00.000000000&#x27;,\n",
+       "       &#x27;2012-09-18T10:00:00.000000000&#x27;, ..., &#x27;2012-09-27T09:30:00.000000000&#x27;,\n",
+       "       &#x27;2012-09-27T10:00:00.000000000&#x27;, &#x27;2012-09-27T10:30:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_ahead</span></div><div class='xr-var-dims'>(t_ahead)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>00:00:00 ... 3 days 23:30:00</div><input id='attrs-72b84d28-4b8c-47d6-aa72-1d5cc50098ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72b84d28-4b8c-47d6-aa72-1d5cc50098ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22bd57db-624c-4db1-a184-b5a451024928' class='xr-var-data-in' type='checkbox'><label for='data-22bd57db-624c-4db1-a184-b5a451024928' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([              0,   1800000000000,   3600000000000,   5400000000000,\n",
        "         7200000000000,   9000000000000,  10800000000000,  12600000000000,\n",
        "        14400000000000,  16200000000000,  18000000000000,  19800000000000,\n",
        "        21600000000000,  23400000000000,  25200000000000,  27000000000000,\n",
@@ -3218,7 +2741,7 @@
        "       324000000000000, 325800000000000, 327600000000000, 329400000000000,\n",
        "       331200000000000, 333000000000000, 334800000000000, 336600000000000,\n",
        "       338400000000000, 340200000000000, 342000000000000, 343800000000000],\n",
-       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_behind</span></div><div class='xr-var-dims'>(t_behind)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>-4 days +00:00:00 ... -1 days +2...</div><input id='attrs-a7b1921f-1fc4-471e-8c88-48ef83937f0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7b1921f-1fc4-471e-8c88-48ef83937f0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e1d987f-215c-4ff4-8e1b-10e03cc9f38c' class='xr-var-data-in' type='checkbox'><label for='data-4e1d987f-215c-4ff4-8e1b-10e03cc9f38c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-345600000000000, -343800000000000, -342000000000000, -340200000000000,\n",
+       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t_behind</span></div><div class='xr-var-dims'>(t_behind)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>-4 days +00:00:00 ... -1 days +2...</div><input id='attrs-58a74bec-799d-4013-bd76-f4e8db21c40b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58a74bec-799d-4013-bd76-f4e8db21c40b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd61437e-50a7-4e19-89c6-9e647a586882' class='xr-var-data-in' type='checkbox'><label for='data-cd61437e-50a7-4e19-89c6-9e647a586882' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-345600000000000, -343800000000000, -342000000000000, -340200000000000,\n",
        "       -338400000000000, -336600000000000, -334800000000000, -333000000000000,\n",
        "       -331200000000000, -329400000000000, -327600000000000, -325800000000000,\n",
        "       -324000000000000, -322200000000000, -320400000000000, -318600000000000,\n",
@@ -3266,55 +2789,55 @@
        "        -21600000000000,  -19800000000000,  -18000000000000,  -16200000000000,\n",
        "        -14400000000000,  -12600000000000,  -10800000000000,   -9000000000000,\n",
        "         -7200000000000,   -5400000000000,   -3600000000000,   -1800000000000],\n",
-       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_target</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-11-19T13:00:00 ... 2013-11-...</div><input id='attrs-456d0c77-491b-4c60-8566-bc5c901f16ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-456d0c77-491b-4c60-8566-bc5c901f16ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e74552c-4fdd-4afb-a610-98c0ec503147' class='xr-var-data-in' type='checkbox'><label for='data-3e74552c-4fdd-4afb-a610-98c0ec503147' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2013-11-19T13:00:00.000000000&#x27;, &#x27;2013-11-19T13:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-19T14:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-23T11:30:00.000000000&#x27;, &#x27;2013-11-23T12:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-23T12:30:00.000000000&#x27;],\n",
-       "       [&#x27;2013-11-19T13:30:00.000000000&#x27;, &#x27;2013-11-19T14:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-19T14:30:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-23T12:00:00.000000000&#x27;, &#x27;2013-11-23T12:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-23T13:00:00.000000000&#x27;],\n",
-       "       [&#x27;2013-11-19T14:00:00.000000000&#x27;, &#x27;2013-11-19T14:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-19T15:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-23T12:30:00.000000000&#x27;, &#x27;2013-11-23T13:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-23T13:30:00.000000000&#x27;],\n",
+       "      dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_target</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-09-18T09:00:00 ... 2012-10-...</div><input id='attrs-57e4a533-79ea-43ea-8104-8d5a4053fde6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57e4a533-79ea-43ea-8104-8d5a4053fde6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cce9e6f0-1a07-41de-b2e3-d838a0e7a0d1' class='xr-var-data-in' type='checkbox'><label for='data-cce9e6f0-1a07-41de-b2e3-d838a0e7a0d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2012-09-18T09:00:00.000000000&#x27;, &#x27;2012-09-18T09:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-18T10:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-22T07:30:00.000000000&#x27;, &#x27;2012-09-22T08:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-22T08:30:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-18T09:30:00.000000000&#x27;, &#x27;2012-09-18T10:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-18T10:30:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-22T08:00:00.000000000&#x27;, &#x27;2012-09-22T08:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-22T09:00:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-18T10:00:00.000000000&#x27;, &#x27;2012-09-18T10:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-18T11:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-22T08:30:00.000000000&#x27;, &#x27;2012-09-22T09:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-22T09:30:00.000000000&#x27;],\n",
        "       ...,\n",
-       "       [&#x27;2013-11-21T09:30:00.000000000&#x27;, &#x27;2013-11-21T10:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-21T10:30:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-25T08:00:00.000000000&#x27;, &#x27;2013-11-25T08:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-25T09:00:00.000000000&#x27;],\n",
-       "       [&#x27;2013-11-21T10:00:00.000000000&#x27;, &#x27;2013-11-21T10:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-21T11:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-25T08:30:00.000000000&#x27;, &#x27;2013-11-25T09:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-25T09:30:00.000000000&#x27;],\n",
-       "       [&#x27;2013-11-21T10:30:00.000000000&#x27;, &#x27;2013-11-21T11:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-21T11:30:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-25T09:00:00.000000000&#x27;, &#x27;2013-11-25T09:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-25T10:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_past</span></div><div class='xr-var-dims'>(t_source, t_behind)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-11-15T13:00:00 ... 2013-11-...</div><input id='attrs-bb746c6b-2a01-4f92-8a4c-5a0291425dcd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb746c6b-2a01-4f92-8a4c-5a0291425dcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdc507e1-40bc-4cc5-935e-65736ef563c3' class='xr-var-data-in' type='checkbox'><label for='data-bdc507e1-40bc-4cc5-935e-65736ef563c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2013-11-15T13:00:00.000000000&#x27;, &#x27;2013-11-15T13:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-15T14:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-19T11:30:00.000000000&#x27;, &#x27;2013-11-19T12:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-19T12:30:00.000000000&#x27;],\n",
-       "       [&#x27;2013-11-15T13:30:00.000000000&#x27;, &#x27;2013-11-15T14:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-15T14:30:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-19T12:00:00.000000000&#x27;, &#x27;2013-11-19T12:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-19T13:00:00.000000000&#x27;],\n",
-       "       [&#x27;2013-11-15T14:00:00.000000000&#x27;, &#x27;2013-11-15T14:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-15T15:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-19T12:30:00.000000000&#x27;, &#x27;2013-11-19T13:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-19T13:30:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-27T09:30:00.000000000&#x27;, &#x27;2012-09-27T10:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-27T10:30:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-10-01T08:00:00.000000000&#x27;, &#x27;2012-10-01T08:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-10-01T09:00:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-27T10:00:00.000000000&#x27;, &#x27;2012-09-27T10:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-27T11:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-10-01T08:30:00.000000000&#x27;, &#x27;2012-10-01T09:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-10-01T09:30:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-27T10:30:00.000000000&#x27;, &#x27;2012-09-27T11:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-27T11:30:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-10-01T09:00:00.000000000&#x27;, &#x27;2012-10-01T09:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-10-01T10:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_past</span></div><div class='xr-var-dims'>(t_source, t_behind)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-09-14T09:00:00 ... 2012-09-...</div><input id='attrs-b9e75fa5-97a6-43d5-b4ae-d26c94de1872' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9e75fa5-97a6-43d5-b4ae-d26c94de1872' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40309db7-948f-4cec-8470-651b07945b03' class='xr-var-data-in' type='checkbox'><label for='data-40309db7-948f-4cec-8470-651b07945b03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2012-09-14T09:00:00.000000000&#x27;, &#x27;2012-09-14T09:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-14T10:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-18T07:30:00.000000000&#x27;, &#x27;2012-09-18T08:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-18T08:30:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-14T09:30:00.000000000&#x27;, &#x27;2012-09-14T10:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-14T10:30:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-18T08:00:00.000000000&#x27;, &#x27;2012-09-18T08:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-18T09:00:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-14T10:00:00.000000000&#x27;, &#x27;2012-09-14T10:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-14T11:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-18T08:30:00.000000000&#x27;, &#x27;2012-09-18T09:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-18T09:30:00.000000000&#x27;],\n",
        "       ...,\n",
-       "       [&#x27;2013-11-17T09:30:00.000000000&#x27;, &#x27;2013-11-17T10:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-17T10:30:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-21T08:00:00.000000000&#x27;, &#x27;2013-11-21T08:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-21T09:00:00.000000000&#x27;],\n",
-       "       [&#x27;2013-11-17T10:00:00.000000000&#x27;, &#x27;2013-11-17T10:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-17T11:00:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-21T08:30:00.000000000&#x27;, &#x27;2013-11-21T09:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-21T09:30:00.000000000&#x27;],\n",
-       "       [&#x27;2013-11-17T10:30:00.000000000&#x27;, &#x27;2013-11-17T11:00:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-17T11:30:00.000000000&#x27;, ...,\n",
-       "        &#x27;2013-11-21T09:00:00.000000000&#x27;, &#x27;2013-11-21T09:30:00.000000000&#x27;,\n",
-       "        &#x27;2013-11-21T10:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ahead_hours</span></div><div class='xr-var-dims'>(t_ahead)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 1.0 1.0 ... 94.0 95.0 95.0</div><input id='attrs-94e20dba-d03a-4fe0-8c90-c5575e27bbb1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94e20dba-d03a-4fe0-8c90-c5575e27bbb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc215c33-d468-40e2-b5d2-420658655863' class='xr-var-data-in' type='checkbox'><label for='data-cc215c33-d468-40e2-b5d2-420658655863' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  1.,  1.,  2.,  2.,  3.,  3.,  4.,  4.,  5.,  5.,  6.,\n",
+       "       [&#x27;2012-09-23T09:30:00.000000000&#x27;, &#x27;2012-09-23T10:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-23T10:30:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-27T08:00:00.000000000&#x27;, &#x27;2012-09-27T08:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-27T09:00:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-23T10:00:00.000000000&#x27;, &#x27;2012-09-23T10:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-23T11:00:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-27T08:30:00.000000000&#x27;, &#x27;2012-09-27T09:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-27T09:30:00.000000000&#x27;],\n",
+       "       [&#x27;2012-09-23T10:30:00.000000000&#x27;, &#x27;2012-09-23T11:00:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-23T11:30:00.000000000&#x27;, ...,\n",
+       "        &#x27;2012-09-27T09:00:00.000000000&#x27;, &#x27;2012-09-27T09:30:00.000000000&#x27;,\n",
+       "        &#x27;2012-09-27T10:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ahead_hours</span></div><div class='xr-var-dims'>(t_ahead)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 1.0 1.0 ... 94.0 95.0 95.0</div><input id='attrs-9107abc0-0e1e-410b-ab79-da8640ab982d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9107abc0-0e1e-410b-ab79-da8640ab982d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f94ec7ce-4485-430e-861b-408c828f2cf0' class='xr-var-data-in' type='checkbox'><label for='data-f94ec7ce-4485-430e-861b-408c828f2cf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  1.,  1.,  2.,  2.,  3.,  3.,  4.,  4.,  5.,  5.,  6.,\n",
        "        6.,  7.,  7.,  8.,  8.,  9.,  9., 10., 10., 11., 11., 12., 12.,\n",
        "       13., 13., 14., 14., 15., 15., 16., 16., 17., 17., 18., 18., 19.,\n",
        "       19., 20., 20., 21., 21., 22., 22., 23., 23., 24., 24., 25., 25.,\n",
@@ -3328,103 +2851,103 @@
        "       71., 72., 72., 73., 73., 74., 74., 75., 75., 76., 76., 77., 77.,\n",
        "       78., 78., 79., 79., 80., 80., 81., 81., 82., 82., 83., 83., 84.,\n",
        "       84., 85., 85., 86., 86., 87., 87., 88., 88., 89., 89., 90., 90.,\n",
-       "       91., 91., 92., 92., 93., 93., 94., 94., 95., 95.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8d60ead-3d4b-4f7c-afae-afafdd07d45a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8d60ead-3d4b-4f7c-afae-afafdd07d45a' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_past</span></div><div class='xr-var-dims'>(t_source, t_behind)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.44799998 0.318 ... 0.454 0.505</div><input id='attrs-1086bba8-f235-4eaa-8c53-54623898e51d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1086bba8-f235-4eaa-8c53-54623898e51d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06932573-8e4d-4c81-99cc-204e76fdbb56' class='xr-var-data-in' type='checkbox'><label for='data-06932573-8e4d-4c81-99cc-204e76fdbb56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44799998, 0.318     , 0.292     , ..., 0.266     , 0.08000001,\n",
-       "        0.119     ],\n",
-       "       [0.318     , 0.292     , 0.082     , ..., 0.08000001, 0.119     ,\n",
-       "        0.07400002],\n",
-       "       [0.292     , 0.082     , 0.12900001, ..., 0.119     , 0.07400002,\n",
-       "        0.118     ],\n",
+       "       91., 91., 92., 92., 93., 93., 94., 94., 95., 95.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed886014-8cf5-40f1-9295-77eb0cfe1fab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed886014-8cf5-40f1-9295-77eb0cfe1fab' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_past</span></div><div class='xr-var-dims'>(t_source, t_behind)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.061000023 ... 0.12699997</div><input id='attrs-63fd23e4-2d6c-40ab-8919-ca7bfe444c4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63fd23e4-2d6c-40ab-8919-ca7bfe444c4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d8b2114-5e5d-4146-bea2-2f0262aaaf47' class='xr-var-data-in' type='checkbox'><label for='data-2d8b2114-5e5d-4146-bea2-2f0262aaaf47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.06100002, 0.04999999, 0.18399999, ..., 0.07999998, 0.083     ,\n",
+       "        0.10499999],\n",
+       "       [0.04999999, 0.18399999, 0.049     , ..., 0.083     , 0.10499999,\n",
+       "        0.17700002],\n",
+       "       [0.18399999, 0.049     , 0.07100001, ..., 0.10499999, 0.17700002,\n",
+       "        0.06799999],\n",
        "       ...,\n",
-       "       [0.376     , 0.45499998, 0.385     , ..., 0.304     , 0.376     ,\n",
-       "        0.505     ],\n",
-       "       [0.45499998, 0.385     , 0.428     , ..., 0.376     , 0.505     ,\n",
-       "        0.454     ],\n",
-       "       [0.385     , 0.428     , 0.308     , ..., 0.505     , 0.454     ,\n",
-       "        0.505     ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nll</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.4400969 1.3171842 ... 3.8363848</div><input id='attrs-48e1d05a-2c11-42b5-808a-cf26bfbca380' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48e1d05a-2c11-42b5-808a-cf26bfbca380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1160171b-66a9-436b-88cc-28317532c3ca' class='xr-var-data-in' type='checkbox'><label for='data-1160171b-66a9-436b-88cc-28317532c3ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.4400969 , 1.3171842 , 1.4024196 , ..., 1.1859143 , 1.1052086 ,\n",
-       "        0.89732474],\n",
-       "       [1.200535  , 1.3452052 , 1.0674341 , ..., 1.1048898 , 0.89672965,\n",
-       "        2.1485229 ],\n",
-       "       [1.2325568 , 1.0395672 , 1.7901232 , ..., 0.89613587, 2.1503088 ,\n",
-       "        0.7932498 ],\n",
+       "       [0.123     , 0.08500001, 0.042     , ..., 0.16300002, 0.141     ,\n",
+       "        0.14899999],\n",
+       "       [0.08500001, 0.042     , 0.042     , ..., 0.141     , 0.14899999,\n",
+       "        0.387     ],\n",
+       "       [0.042     , 0.042     , 0.06200001, ..., 0.14899999, 0.387     ,\n",
+       "        0.12699997]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nll</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.44390392 ... 0.79414505</div><input id='attrs-8842d81c-d15c-42ff-9aed-cc44fb9317b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8842d81c-d15c-42ff-9aed-cc44fb9317b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ebbd98b-53b3-47b6-9fda-d18b6d552a68' class='xr-var-data-in' type='checkbox'><label for='data-5ebbd98b-53b3-47b6-9fda-d18b6d552a68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44390392, 0.56664157, 0.5809205 , ..., 0.41122854, 0.56711197,\n",
+       "        0.58973217],\n",
+       "       [0.6743895 , 0.6417631 , 0.5153675 , ..., 0.56559354, 0.588212  ,\n",
+       "        0.6929046 ],\n",
+       "       [0.7600697 , 0.5740746 , 0.5059802 , ..., 0.58670086, 0.6912286 ,\n",
+       "        0.66683286],\n",
        "       ...,\n",
-       "       [1.312731  , 1.2981818 , 1.0142597 , ..., 1.3494017 , 1.4450157 ,\n",
-       "        4.714029  ],\n",
-       "       [1.270231  , 1.0229378 , 1.33813   , ..., 1.4448819 , 4.7149496 ,\n",
-       "        3.4682097 ],\n",
-       "       [1.0256569 , 1.2915292 , 1.7793853 , ..., 4.715878  , 3.468279  ,\n",
-       "        3.8363848 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_pred</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.3194818 0.32155803 ... 0.39490056</div><input id='attrs-082e90d1-f5bc-4eda-831c-9e88348f4c86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-082e90d1-f5bc-4eda-831c-9e88348f4c86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8b9d09b-1b16-4425-b768-960ef8cc12d5' class='xr-var-data-in' type='checkbox'><label for='data-a8b9d09b-1b16-4425-b768-960ef8cc12d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.3194818 , 0.32155803, 0.32646868, ..., 0.28943852, 0.2870614 ,\n",
-       "        0.266677  ],\n",
-       "       [0.29126933, 0.3105985 , 0.31399   , ..., 0.28694892, 0.26658514,\n",
-       "        0.2526558 ],\n",
-       "       [0.28419635, 0.30168998, 0.3167666 , ..., 0.26649326, 0.25256333,\n",
-       "        0.23280898],\n",
+       "       [0.43764848, 0.706943  , 0.6868087 , ..., 0.6452905 , 0.5894213 ,\n",
+       "        0.44566935],\n",
+       "       [0.72706467, 0.6857369 , 0.4967181 , ..., 0.5890883 , 0.4456083 ,\n",
+       "        0.7410833 ],\n",
+       "       [0.7192894 , 0.47428977, 0.37764883, ..., 0.44555452, 0.7405691 ,\n",
+       "        0.79414505]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_pred</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.33915406 0.3045206 ... 0.37555674</div><input id='attrs-07e2903b-5c3e-4514-87a2-7d817cde8d2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07e2903b-5c3e-4514-87a2-7d817cde8d2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bacf1c4-0bb9-43ef-b5a7-df34e08438be' class='xr-var-data-in' type='checkbox'><label for='data-4bacf1c4-0bb9-43ef-b5a7-df34e08438be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.33915406, 0.3045206 , 0.28648722, ..., 0.30625796, 0.3149696 ,\n",
+       "        0.3200055 ],\n",
+       "       [0.34753686, 0.31381938, 0.2943569 , ..., 0.31429982, 0.31933692,\n",
+       "        0.32741007],\n",
+       "       [0.35634485, 0.323293  , 0.3040018 , ..., 0.3186717 , 0.3267355 ,\n",
+       "        0.33097428],\n",
        "       ...,\n",
-       "       [0.37676603, 0.37938213, 0.30118755, ..., 0.38836744, 0.39958474,\n",
-       "        0.39222595],\n",
-       "       [0.3778184 , 0.29442453, 0.27112436, ..., 0.39961642, 0.3922611 ,\n",
-       "        0.4035997 ],\n",
-       "       [0.3008607 , 0.27245116, 0.22872949, ..., 0.39229608, 0.40363526,\n",
-       "        0.39490056]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_pred_std</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2927 0.2852 ... 0.4196 0.3664</div><input id='attrs-250e2592-039f-4f22-97a2-d9a339f17a0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-250e2592-039f-4f22-97a2-d9a339f17a0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22e8a5a7-9300-463e-b1b4-c6f770babc0f' class='xr-var-data-in' type='checkbox'><label for='data-22e8a5a7-9300-463e-b1b4-c6f770babc0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.29270671, 0.28521932, 0.278733  , ..., 0.26677684, 0.2661316 ,\n",
-       "        0.24011513],\n",
-       "       [0.26397978, 0.27060238, 0.27201789, ..., 0.26595358, 0.23995925,\n",
-       "        0.2253197 ],\n",
-       "       [0.24808435, 0.26783952, 0.27565007, ..., 0.23980372, 0.22516666,\n",
-       "        0.20325121],\n",
+       "       [0.4101906 , 0.394241  , 0.3390931 , ..., 0.36369714, 0.3686615 ,\n",
+       "        0.369526  ],\n",
+       "       [0.411724  , 0.34518054, 0.28593865, ..., 0.36847636, 0.36931273,\n",
+       "        0.37449035],\n",
+       "       [0.36108527, 0.28604382, 0.23601256, ..., 0.36910504, 0.37427023,\n",
+       "        0.37555674]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_pred_std</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3371 0.332 ... 0.3789 0.379</div><input id='attrs-cb682f41-5383-4a43-885f-adfc5f822e83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb682f41-5383-4a43-885f-adfc5f822e83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7edd9842-d80e-4c8f-8411-374546f97737' class='xr-var-data-in' type='checkbox'><label for='data-7edd9842-d80e-4c8f-8411-374546f97737' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.33712136, 0.33197249, 0.33133024, ..., 0.3487567 , 0.3522541 ,\n",
+       "        0.35496513],\n",
+       "       [0.33966592, 0.32810577, 0.33189367, ..., 0.35206261, 0.35478147,\n",
+       "        0.35801704],\n",
+       "       [0.33683602, 0.3314646 , 0.33128671, ..., 0.35459882, 0.35782855,\n",
+       "        0.3600166 ],\n",
        "       ...,\n",
-       "       [0.35787888, 0.33675621, 0.20524449, ..., 0.36760824, 0.41779645,\n",
-       "        0.36737644],\n",
-       "       [0.32522555, 0.1959869 , 0.15263464, ..., 0.41774172, 0.36730234,\n",
-       "        0.41965691],\n",
-       "       [0.20996562, 0.16055321, 0.11981557, ..., 0.36722782, 0.41962386,\n",
-       "        0.36638736]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_true</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.074000016 0.118 ... 1.2180002</div><input id='attrs-0ca93f96-2026-48cd-9c95-ddd1c067ce64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ca93f96-2026-48cd-9c95-ddd1c067ce64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28fca5bf-2401-4327-a425-edd472c7e5c9' class='xr-var-data-in' type='checkbox'><label for='data-28fca5bf-2401-4327-a425-edd472c7e5c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.07400002, 0.118     , 0.089     , ..., 0.454     , 0.413     ,\n",
-       "        0.29500002],\n",
-       "       [0.118     , 0.089     , 0.226     , ..., 0.413     , 0.29500002,\n",
-       "        0.619     ],\n",
-       "       [0.089     , 0.226     , 0.657     , ..., 0.29500002, 0.619     ,\n",
-       "        0.157     ],\n",
+       "       [0.37538716, 0.38870546, 0.3608304 , ..., 0.37574382, 0.37732049,\n",
+       "        0.37726724],\n",
+       "       [0.37861461, 0.34732474, 0.33039895, ..., 0.37728526, 0.37722251,\n",
+       "        0.37900413],\n",
+       "       [0.34116173, 0.31832921, 0.30483951, ..., 0.37718101, 0.37894148,\n",
+       "        0.3790149 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_true</span></div><div class='xr-var-dims'>(t_source, t_ahead)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.17700002 ... 0.05900002</div><input id='attrs-7c3362eb-c3bc-4d60-99d0-7c0e4c035678' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c3362eb-c3bc-4d60-99d0-7c0e4c035678' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a79bfefc-9662-4e25-8391-68d6ae5ed856' class='xr-var-data-in' type='checkbox'><label for='data-a79bfefc-9662-4e25-8391-68d6ae5ed856' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.17700002, 0.06799999, 0.04300002, ..., 0.197     , 0.095     ,\n",
+       "        0.09      ],\n",
+       "       [0.06799999, 0.04300002, 0.083     , ..., 0.095     , 0.09      ,\n",
+       "        0.04799998],\n",
+       "       [0.04300002, 0.083     , 0.097     , ..., 0.09      , 0.04799998,\n",
+       "        0.065     ],\n",
        "       ...,\n",
-       "       [0.454     , 0.505     , 0.45499998, ..., 0.483     , 0.416     ,\n",
-       "        1.35      ],\n",
-       "       [0.505     , 0.45499998, 0.47599998, ..., 0.416     , 1.35      ,\n",
-       "        1.247     ],\n",
-       "       [0.45499998, 0.47599998, 0.442     , ..., 1.35      , 1.247     ,\n",
-       "        1.2180002 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e10c59ee-821a-479d-9c00-bf464aa66f24' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e10c59ee-821a-479d-9c00-bf464aa66f24' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       [0.387     , 0.12699997, 0.06400001, ..., 0.12099999, 0.16300002,\n",
+       "        0.407     ],\n",
+       "       [0.12699997, 0.06400001, 0.083     , ..., 0.16300002, 0.407     ,\n",
+       "        0.083     ],\n",
+       "       [0.06400001, 0.083     , 0.06900001, ..., 0.407     , 0.083     ,\n",
+       "        0.05900002]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e5182a16-4d35-48b5-a82d-c30b564ec810' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5182a16-4d35-48b5-a82d-c30b564ec810' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:        (t_ahead: 192, t_behind: 192, t_source: 4439)\n",
+       "Dimensions:        (t_ahead: 192, t_behind: 192, t_source: 24943)\n",
        "Coordinates:\n",
-       "  * t_source       (t_source) datetime64[ns] 2013-11-19T13:00:00 ... 2013-11-...\n",
+       "  * t_source       (t_source) datetime64[ns] 2012-09-18T09:00:00 ... 2012-09-...\n",
        "  * t_ahead        (t_ahead) timedelta64[ns] 00:00:00 ... 3 days 23:30:00\n",
        "  * t_behind       (t_behind) timedelta64[ns] -4 days +00:00:00 ... -1 days +...\n",
-       "    t_target       (t_source, t_ahead) datetime64[ns] 2013-11-19T13:00:00 ......\n",
-       "    t_past         (t_source, t_behind) datetime64[ns] 2013-11-15T13:00:00 .....\n",
+       "    t_target       (t_source, t_ahead) datetime64[ns] 2012-09-18T09:00:00 ......\n",
+       "    t_past         (t_source, t_behind) datetime64[ns] 2012-09-14T09:00:00 .....\n",
        "    t_ahead_hours  (t_ahead) float64 0.0 0.0 1.0 1.0 2.0 ... 94.0 94.0 95.0 95.0\n",
        "Data variables:\n",
-       "    y_past         (t_source, t_behind) float32 0.44799998 0.318 ... 0.454 0.505\n",
-       "    nll            (t_source, t_ahead) float32 1.4400969 1.3171842 ... 3.8363848\n",
-       "    y_pred         (t_source, t_ahead) float32 0.3194818 ... 0.39490056\n",
-       "    y_pred_std     (t_source, t_ahead) float64 0.2927 0.2852 ... 0.4196 0.3664\n",
-       "    y_true         (t_source, t_ahead) float32 0.074000016 0.118 ... 1.2180002"
+       "    y_past         (t_source, t_behind) float32 0.061000023 ... 0.12699997\n",
+       "    nll            (t_source, t_ahead) float32 0.44390392 ... 0.79414505\n",
+       "    y_pred         (t_source, t_ahead) float32 0.33915406 ... 0.37555674\n",
+       "    y_pred_std     (t_source, t_ahead) float64 0.3371 0.332 ... 0.3789 0.379\n",
+       "    y_true         (t_source, t_ahead) float32 0.17700002 ... 0.05900002"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ds_preds = predict(model.to(device), ds_test, batch_size, device=device, scaler=output_scaler)\n",
+    "ds_preds = predict(model.to(device), ds_test.datasets[0], batch_size, device=device, scaler=output_scaler)\n",
     "ds_preds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 44,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:14:57.046775Z",
-     "start_time": "2020-10-18T07:14:56.988468Z"
+     "end_time": "2020-10-18T07:30:35.342452Z",
+     "start_time": "2020-10-18T07:30:35.292433Z"
     }
    },
    "outputs": [],
@@ -3434,36 +2957,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 52,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:14:57.891184Z",
-     "start_time": "2020-10-18T07:14:57.050445Z"
+     "end_time": "2020-10-18T07:32:00.258755Z",
+     "start_time": "2020-10-18T07:31:59.385632Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -3544,34 +3063,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 53,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:14:58.192879Z",
-     "start_time": "2020-10-18T07:14:57.893583Z"
+     "end_time": "2020-10-18T07:32:01.574900Z",
+     "start_time": "2020-10-18T07:32:01.183728Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 1.0, 'NLL vs time (no. samples=4439)')"
+       "Text(0.5, 1.0, 'NLL vs time (no. samples=24943)')"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x288 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -3599,11 +3116,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 54,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:14:59.037853Z",
-     "start_time": "2020-10-18T07:14:58.195017Z"
+     "end_time": "2020-10-18T07:32:04.262783Z",
+     "start_time": "2020-10-18T07:32:01.966295Z"
     }
    },
    "outputs": [
@@ -3613,20 +3130,18 @@
        "1"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x288 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -3640,34 +3155,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 55,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-18T07:14:59.695795Z",
-     "start_time": "2020-10-18T07:14:59.040612Z"
+     "end_time": "2020-10-18T07:32:06.259237Z",
+     "start_time": "2020-10-18T07:32:04.265934Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f041437e810>"
+       "<matplotlib.collections.PathCollection at 0x7f8f007238d0>"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x288 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
diff --git a/notebooks/RNN_Timeseries_Seq2Seq.py b/notebooks/RNN_Timeseries_Seq2Seq.py
index b3aacd1..aa58bbb 100644
--- a/notebooks/RNN_Timeseries_Seq2Seq.py
+++ b/notebooks/RNN_Timeseries_Seq2Seq.py
@@ -55,6 +55,8 @@ import torch.utils.data
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
+plt.rcParams['figure.figsize'] = (12.0, 4.0)
+plt.style.use('ggplot')
 
 from pathlib import Path
 from tqdm.auto import tqdm
@@ -158,11 +160,11 @@ dfs = list(dfs)
 # df = df.resample(freq).first().dropna() # Where empty we will backfill, this will respect causality, and mostly maintain the mean
 
 # df = df.tail(int(max_rows)).copy() # Just use last X rows
-# df = pd.concat(dfs, 0)
-df = dfs[0]
+df = pd.concat(dfs[:6], 0)
+# df = dfs[0]
 # -
 
-
+df.LCLid.unique()
 
 
 
@@ -188,22 +190,22 @@ df_norm
 output_scaler = next(filter(lambda r:r[0][0] in columns_target, scaler.features))[-1]
 output_scaler
 
-# # Resample
-df_norm = df_norm.resample(freq).first().fillna(0)
+dfs_norm = [d.resample(freq).first().ffill().dropna() for _, d in df_norm.groupby('LCLid')]
+len(dfs_norm)
 
 # +
 # split data, with the test in the future
-n_split = -int(len(df)*0.2)
-df_train = df_norm[:n_split]
-df_test = df_norm[n_split:]
+n_split = -int(len(dfs_norm)*0.2)
+df_train = dfs_norm[:n_split]
+df_test = dfs_norm[n_split:]
 
 # Show split
-df_train['energy(kWh/hh)'].plot(label='train')
-df_test['energy(kWh/hh)'].plot(label='test')
+pd.concat(df_train)['energy(kWh/hh)'].plot(label='train')
+pd.concat(df_test)['energy(kWh/hh)'].plot(label='test')
 plt.ylabel('energy(kWh/hh)')
 plt.legend()
 # -
-df_norm
+
 
 
 # ### Dataset
@@ -214,11 +216,11 @@ columns_blank=['visibility',
        'windBearing', 'temperature', 'dewPoint', 'pressure',
        'apparentTemperature', 'windSpeed', 'humidity']
 
-ds_train = Seq2SeqDataSet(df_train,
+ds_train = Seq2SeqDataSets(df_train,
                           window_past=window_past,
                           window_future=window_future,
                           columns_blank=columns_blank)
-ds_test = Seq2SeqDataSet(df_test,
+ds_test = Seq2SeqDataSets(df_test,
                          window_past=window_past,
                          window_future=window_future,
                          columns_blank=columns_blank)
@@ -228,7 +230,7 @@ print(ds_test)
 # we can treat it like an array
 ds_train[0]
 len(ds_train)
-ds_train[0]
+ds_train[-1]
 
 # +
 # We can get rows
@@ -325,7 +327,6 @@ class PL_Seq2Seq(pl.LightningModule):
         return torch.optim.Adam(self.parameters(), lr=1e-4)
 
 
-
 # -
 
 from torch.utils.data import DataLoader, random_split
@@ -341,9 +342,9 @@ output_size = y_future.shape[-1]
 model = PL_Seq2Seq(input_size=input_size,
                    input_size_decoder=input_size,
                    output_size=output_size,
-                   hidden_size=16,
-                   lstm_layers=1,
-                   lstm_dropout=0.5).to(device)
+                   hidden_size=32,
+                   lstm_layers=2,
+                   lstm_dropout=0.25).to(device)
 
 logger = CSVLogger("logs", name="seq2seq")
 trainer = pl.Trainer(gpus=1,
@@ -351,7 +352,7 @@ trainer = pl.Trainer(gpus=1,
 dl_train = DataLoader(ds_train,
                       batch_size=batch_size,
                       shuffle=True,
-                      num_workers=4)
+                      num_workers=8)
 dl_test = DataLoader(ds_test, batch_size=batch_size, num_workers=4)
 trainer.fit(model, dl_train, dl_test)
 # -
@@ -364,7 +365,7 @@ df_histe
 # ## Predict
 #
 
-ds_preds = predict(model.to(device), ds_test, batch_size, device=device, scaler=output_scaler)
+ds_preds = predict(model.to(device), ds_test.datasets[0], batch_size, device=device, scaler=output_scaler)
 ds_preds
 
 
diff --git a/seq2seq_time/data/dataset.py b/seq2seq_time/data/dataset.py
index e574fcf..b793bdf 100644
--- a/seq2seq_time/data/dataset.py
+++ b/seq2seq_time/data/dataset.py
@@ -115,7 +115,7 @@ class Seq2SeqDataSets(torch.utils.data.Dataset):
         for d in self.datasets:
             l += len(d)
             if i < l:
-                return d[i]
+                return d[l-i]
         raise IndexError
 
     def get_rows(self, i):
@@ -123,7 +123,7 @@ class Seq2SeqDataSets(torch.utils.data.Dataset):
         for d in self.datasets:
             l += len(d)
             if i < l:
-                return d.get_rows(i)
+                return d.get_rows(l-i)
         raise IndexError
 
     def __len__(self):