Metadata-Version: 2.1
Name: nbeats-pytorch
Version: 1.3.3
Summary: N-Beats
Home-page: UNKNOWN
Author: Philippe Remy (Pytorch), Jean Sebastien Dhr (Keras)
License: MIT
Description: # N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
        - *Implementation in Keras by @eljdos (Jean-Sébastien Dhr)*
        - *Implementation in Pytorch by @philipperemy (Philippe Remy)*
        - https://arxiv.org/abs/1905.10437
        
        <p align="center">
          <img src="nbeats.png" width="600"><br/>
          <i>N-Beats at the beginning of the training</i><br><br>
        </p>
        
        Trust me, after a few more steps, the green curve (predictions) matches the ground truth exactly :-)
        
        ## Installation
        
        ### From PyPI
        
        Install Keras: `pip install nbeats-keras`.
        
        Install Pytorch: `pip install nbeats-pytorch`.
        
        ### From the sources
        
        Installation is based on a MakeFile. Make sure you are in a virtualenv and have python3 installed.
        
        Command to install N-Beats with Keras: `make install-keras`
        
        Command to install N-Beats with Pytorch: `make install-pytorch`
        
        ### Run on the GPU
        
        To force the utilization of the GPU, run: `pip uninstall -y tensorflow && pip install tensorflow-gpu`.
        
        ## Example
        
        Jupyter notebook: [NBeats.ipynb](examples/NBeats.ipynb): `make run-jupyter`.
        
        Here is a toy example on how to use this model (train and predict):
        
        ```python
        import numpy as np
        
        from nbeats_keras.model import NBeatsNet
        
        
        def main():
            # https://keras.io/layers/recurrent/
            num_samples, time_steps, input_dim, output_dim = 50_000, 10, 1, 1
        
            # Definition of the model.
            model = NBeatsNet(backcast_length=time_steps, forecast_length=output_dim,
                              stack_types=(NBeatsNet.GENERIC_BLOCK, NBeatsNet.GENERIC_BLOCK), nb_blocks_per_stack=2,
                              thetas_dim=(4, 4), share_weights_in_stack=True, hidden_layer_units=64)
        
            # Definition of the objective function and the optimizer.
            model.compile_model(loss='mae', learning_rate=1e-5)
        
            # Definition of the data. The problem to solve is to find f such as | f(x) - y | -> 0.
            x = np.random.uniform(size=(num_samples, time_steps, input_dim))
            y = np.mean(x, axis=1, keepdims=True)
        
            # Split data into training and testing datasets.
            c = num_samples // 10
            x_train, y_train, x_test, y_test = x[c:], y[c:], x[:c], y[:c]
        
            # Train the model.
            model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=2, batch_size=128)
        
            # Save the model for later.
            model.save('n_beats_model.h5')
        
            # Predict on the testing set.
            predictions = model.predict(x_test)
            print(predictions.shape)
        
            # Load the model.
            model2 = NBeatsNet.load('n_beats_model.h5')
        
            predictions2 = model2.predict(x_test)
            np.testing.assert_almost_equal(predictions, predictions2)
        
        
        if __name__ == '__main__':
            main()
        ```
        
        ## Citation
        
        ```
        @misc{NBeatsPRemy,
          author = {Philippe Remy},
          title = {N-BEATS: Neural basis expansion analysis for interpretable time series forecasting},
          year = {2020},
          publisher = {GitHub},
          journal = {GitHub repository},
          howpublished = {\url{https://github.com/philipperemy/n-beats}},
        }
        ```
        
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