https://github.com/philipperemy/n-beats

Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
https://github.com/philipperemy/n-beats

deep-learning neural-networks pytorch series-forecasting

Last synced: about 2 months ago
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Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

• Host: GitHub
• URL: https://github.com/philipperemy/n-beats
• Owner: philipperemy
• License: mit
• Created: 2019-07-24T05:59:51.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-03-03T02:21:29.000Z (over 1 year ago)
• Last Synced: 2024-07-20T02:35:32.062Z (2 months ago)
• Topics: deep-learning, neural-networks, pytorch, series-forecasting
• Language: Python
• Homepage:
• Size: 209 MB
• Stars: 849
• Watchers: 22
• Forks: 165
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

README

        
## NBEATS
Neural basis expansion analysis for interpretable time series forecasting

Tensorflow/Pytorch implementation | [Paper](https://arxiv.org/abs/1905.10437)

| [Results](https://github.com/fecet/NBeats-M4)

![NBeats CI](https://github.com/philipperemy/n-beats/workflows/N%20Beats%20CI/badge.svg?branch=master)



  


  Outputs of the generic and interpretable layers of NBEATS



### Installation

It is possible to install the two backends at the same time.

#### From PyPI

Install the Tensorflow/Keras backend: `pip install nbeats-keras`

[![NBEATS - Keras - Downloads](https://pepy.tech/badge/nbeats-keras)](https://pepy.tech/project/nbeats-keras)

Install the Pytorch backend: `pip install nbeats-pytorch`

[![NBEATS - PyTorch - Downloads](https://pepy.tech/badge/nbeats-pytorch)](https://pepy.tech/project/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

This trick is no longer necessary on the recent versions of Tensorflow.  To force the utilization of the GPU (with the Keras backend),

run: `pip uninstall -y tensorflow && pip install tensorflow-gpu`. 

### Example

Here is an example to get familiar with both backends. Note that only the Keras backend supports `input_dim>1` at the moment.

```python

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]:

        # NOTE: If you choose the Keras backend with input_dim>1, you have 

        # to set the value here too (in the constructor).

        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()

```

Browse the [examples](examples) for more. It includes Jupyter notebooks.

Jupyter notebook: [NBeats.ipynb](examples/NBeats.ipynb): `make run-jupyter`.

### 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}},

}

```

### Contributors

Thank you!