diff --git a/README.md b/README.md
index 322ed1c..47f01f9 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,106 @@
 # PyTorch-TS
 
-PyTorch-TS is a PyTorch Probabilistic Time Series Modeling framework which provides state of the art time series models and utilities for loading and iterating over time series datasets.
+PyTorch-TS is a PyTorch Probabilistic Time Series Modeling framework which provides state of the art time series models and utilities for loading and iterating over time series data sets.
 
 ## Installation
 
 ```
-$ pip3 install pytorch-ts
+$ pip3 install pytorchts
 ```
 
-## Usage
+## Quick start
 
 ```python
+import matplotlib.pyplot as plt
+
 import pandas as pd
 
 from pts.dataset import ListDataset
+```
 
-url = "https://raw.githubusercontent.com/numenta/NAB/master/data/realTweets/Twitter_volume_AAPL.csv"
-df = pd.read_csv(url, header=0, index_col=0)
 
+```python
+from pts.model.deepar import DeepAREstimator
+from pts import Trainer
+from pts.dataset import to_pandas
+```
+
+This simple example illustrates how to train a model from GluonTS on some data, and then use it to make predictions. As a first step, we need to collect some data: in this example we will use the volume of tweets mentioning the AMZN ticker symbol.
+
+
+```python
+url = "https://raw.githubusercontent.com/numenta/NAB/master/data/realTweets/Twitter_volume_AMZN.csv"
+df = pd.read_csv(url, header=0, index_col=0, parse_dates=True)
+```
+
+The first 100 data points look like follows:
+
+
+```python
+df[:100].plot(linewidth=2)
+plt.grid(which='both')
+plt.show()
+```
+
+![png](examples/images/readme_0.png)
+
+
+We can now prepare a training dataset for our model to train on. Datasets are essentially iterable collections of dictionaries: each dictionary represents a time series with possibly associated features. For this example, we only have one entry, specified by the `"start"` field which is the timestamp of the first data point, and the `"target"` field containing time series data. For training, we will use data up to midnight on April 5th, 2015.
+
+
+```python
 training_data = ListDataset(
     [{"start": df.index[0], "target": df.value[:"2015-04-05 00:00:00"]}],
     freq = "5min"
 )
 ```
 
+A forecasting model is a *predictor* object. One way of obtaining predictors is by training a correspondent estimator. Instantiating an estimator requires specifying the frequency of the time series that it will handle, as well as the number of time steps to predict. In our example we're using 5 minutes data, so `req="5min"`, and we will train a model to predict the next hour, so `prediction_length=12`. The input to the model will be a vector of size `input_size=43` at each time point.  We also specify some minimal training options.
+
+
+```python
+estimator = DeepAREstimator(freq="5min", prediction_length=12, input_size=43, trainer=Trainer(epochs=10))
+predictor = estimator.train(training_data=training_data)
+
+
+    47it [00:02, 16.03it/s, avg_epoch_loss=4.69, epoch=0]
+    48it [00:03, 15.55it/s, avg_epoch_loss=4.22, epoch=1]
+    47it [00:02, 16.87it/s, avg_epoch_loss=4.13, epoch=2]
+    49it [00:03, 15.99it/s, avg_epoch_loss=4.08, epoch=3]
+    49it [00:02, 17.39it/s, avg_epoch_loss=4.04, epoch=4]
+    49it [00:03, 16.07it/s, avg_epoch_loss=4.01, epoch=5]
+    48it [00:03, 15.63it/s, avg_epoch_loss=4.00, epoch=6]   
+    47it [00:02, 15.81it/s, avg_epoch_loss=3.99, epoch=7]
+    49it [00:03, 15.84it/s, avg_epoch_loss=3.98, epoch=8]
+    49it [00:02, 18.14it/s, avg_epoch_loss=3.97, epoch=9]
+```
+
+During training, useful information about the progress will be displayed. To get a full overview of the available options, please refer to the source code of `DeepAREstimator` (or other estimators) and `Trainer`.
+
+We're now ready to make predictions: we will forecast the hour following the midnight on April 15th, 2015.
+
+
+```python
+test_data = ListDataset(
+    [{"start": df.index[0], "target": df.value[:"2015-04-15 00:00:00"]}],
+    freq = "5min"
+)
+```
+
+
+```python
+for test_entry, forecast in zip(test_data, predictor.predict(test_data)):
+    to_pandas(test_entry)[-60:].plot(linewidth=2)
+    forecast.plot(color='g', prediction_intervals=[50.0, 90.0])
+plt.grid(which='both')
+```
+
+![png](examples/images/readme_1.png)
+
+
+Note that the forecast is displayed in terms of a probability distribution: the shaded areas represent the 50% and 90% prediction intervals, respectively, centered around the median (dark green line).
+
+
 ## Development
 
 ```
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diff --git a/setup.py b/setup.py
index f6c1223..f0f8818 100644
--- a/setup.py
+++ b/setup.py
@@ -1,14 +1,14 @@
 from setuptools import setup, find_packages
 
 setup(
-    name='pytorch-ts',
+    name='pytorchts',
     version='0.1.0',
     description="PyTorch Probabilistic Time Series Modeling framework",
     long_description=open("README.md").read(),
     long_description_content_type="text/markdown",
 
     url='https://github.com/kashif/pytorch-ts',
-    license='Apache License Version 2.0',
+    license='MIT',
 
     packages=find_packages(exclude=["tests"]),
     include_package_data=True,