Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

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Keras/Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. |

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Readme

Neural basis expansion analysis for interpretable time series forecasting

Tensorflow/Pytorch implementation | Paper | Results

*Outputs of the generic and interpretable layers of NBEATS*

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

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

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

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`

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`

.

Here is an example to get familiar with both 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]:
# 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 for more. It includes Jupyter notebooks.

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

.

```
@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}},
}
```

Thank you!

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