added back fourier date feature

pts/feature/fourier_date_feature.py

@@ -0,0 +1,69 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+from typing import List
+
+import numpy as np
+import pandas as pd
+from pandas.tseries.frequencies import to_offset
+
+from gluonts.core.component import validated
+from gluonts.time_feature import TimeFeature
+
+
+class FourierDateFeatures(TimeFeature):
+    @validated()
+    def __init__(self, freq: str) -> None:
+        super().__init__()
+        # reocurring freq
+        freqs = [
+            "month",
+            "day",
+            "hour",
+            "minute",
+            "weekofyear",
+            "weekday",
+            "dayofweek",
+            "dayofyear",
+            "daysinmonth",
+        ]
+
+        assert freq in freqs
+        self.freq = freq
+
+    def __call__(self, index: pd.DatetimeIndex) -> np.ndarray:
+        values = getattr(index, self.freq)
+        num_values = max(values) + 1
+        steps = [x * 2.0 * np.pi / num_values for x in values]
+        return np.vstack([np.cos(steps), np.sin(steps)])
+
+def fourier_time_features_from_frequency(freq_str: str) -> List[TimeFeature]:
+    offset = to_offset(freq_str)
+    multiple, granularity = offset.n, offset.name
+
+    features = {
+        "M": ["weekofyear"],
+        "W": ["daysinmonth", "weekofyear"],
+        "D": ["dayofweek"],
+        "B": ["dayofweek", "dayofyear"],
+        "H": ["hour", "dayofweek"],
+        "min": ["minute", "hour", "dayofweek"],
+        "T": ["minute", "hour", "dayofweek"],
+    }
+
+    assert granularity in features, f"freq {granularity} not supported"
+
+    feature_classes: List[TimeFeature] = [
+        FourierDateFeatures(freq=freq) for freq in features[granularity]
+    ]
+    return feature_classes

pts/feature/lags.py

@@ -0,0 +1,28 @@
+from typing import List, Optional
+
+from pandas.tseries.frequencies import to_offset
+
+
+def lags_for_fourier_time_features_from_frequency(
+    freq_str: str, num_lags: Optional[int] = None
+) -> List[int]:
+    offset = to_offset(freq_str)
+    multiple, granularity = offset.n, offset.name
+
+    if granularity == "M":
+        lags = [[1, 12]]
+    elif granularity == "D":
+        lags = [[1, 7, 14]]
+    elif granularity == "B":
+        lags = [[1, 2]]
+    elif granularity == "H":
+        lags = [[1, 24, 168]]
+    elif granularity == "min":
+        lags = [[1, 4, 12, 24, 48]]
+    else:
+        lags = [[1]]
+
+    # use less lags
+    output_lags = list([int(lag) for sub_list in lags for lag in sub_list])
+    output_lags = sorted(list(set(output_lags)))
+    return output_lags[:num_lags]