nbeats-pytorch 1.5.0

N-Beats

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (Keras, Pytorch)

Link to [paper]. Authors: Philippe Remy and Jean-Sebastien Dhr.

/	Tensorflow (2.3, 2.4, 2.5, 2.6)	Pytorch
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N-Beats at the beginning of the training

Trust me, after a few more steps, the green curve (predictions) matches the ground truth exactly :-)

Installation

Make sure you are in a virtualenv (recommended) and have python3 installed.

From PyPI

Install Keras: pip install nbeats-keras.

Install Pytorch: pip install nbeats-pytorch.

From the sources

Installation is based on a MakeFile.

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 (with the Keras backend), run: pip uninstall -y tensorflow && pip install tensorflow-gpu.

Example

Jupyter notebook: NBeats.ipynb: make run-jupyter.

Here is a toy example on how to use this model (train/inference) for the Keras and Pytorch backends.

Note that only the Keras backend supports input_dim>1 (at the moment).

import warnings

import numpy as np

from nbeats_keras.model import NBeatsNet as NBeatsKeras
from nbeats_pytorch.model import NBeatsNet as NBeatsPytorch

warnings.filterwarnings(action='ignore', message='Setting attributes')


def main():
    # https://keras.io/layers/recurrent/
    # At the moment only Keras supports input_dim > 1. In the original paper, input_dim=1.
    num_samples, time_steps, input_dim, output_dim = 50_000, 10, 1, 1

    # This example is for both Keras and Pytorch. In practice, choose the one you prefer.
    for BackendType in [NBeatsKeras, NBeatsPytorch]:
        backend = BackendType(
            backcast_length=time_steps, forecast_length=output_dim,
            stack_types=(NBeatsKeras.GENERIC_BLOCK, NBeatsKeras.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.
        backend.compile(loss='mae', optimizer='adam')

        # Definition of the data. The problem to solve is to find f such as | f(x) - y | -> 0.
        # where f = np.mean.
        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]
        test_size = len(x_test)

        # Train the model.
        print('Training...')
        backend.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=20, batch_size=128)

        # Save the model for later.
        backend.save('n_beats_model.h5')

        # Predict on the testing set (forecast).
        predictions_forecast = backend.predict(x_test)
        np.testing.assert_equal(predictions_forecast.shape, (test_size, backend.forecast_length, output_dim))

        # Predict on the testing set (backcast).
        predictions_backcast = backend.predict(x_test, return_backcast=True)
        np.testing.assert_equal(predictions_backcast.shape, (test_size, backend.backcast_length, output_dim))

        # Load the model.
        model_2 = BackendType.load('n_beats_model.h5')

        np.testing.assert_almost_equal(predictions_forecast, model_2.predict(x_test))


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}},
}