The n-beats from philippechatigny

n-beats's Introduction

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Implementation in Pytorch
https://arxiv.org/abs/1905.10437

N-Beats at the beginning of the training

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

Model

It looks like that:

NBeatsNet(stack_types=[TREND_BLOCK, SEASONALITY_BLOCK], nb_blocks_per_stack=3, forecast_length=5, backcast_length=10, thetas_dims=[4, 8], share_weights_in_stack=False, hidden_layer_units=256)

--- Model ---
| N-Beats
| --  Stack Trend (#0) (share_weights_in_stack=False)
     | -- TrendBlock(units=128, thetas_dim=2, backcast_length=50, forecast_length=10, share_thetas=True) at @4500902576
     | -- TrendBlock(units=128, thetas_dim=2, backcast_length=50, forecast_length=10, share_thetas=True) at @4779951944
     | -- TrendBlock(units=128, thetas_dim=2, backcast_length=50, forecast_length=10, share_thetas=True) at @4779952280
| --  Stack Seasonality (#1) (share_weights_in_stack=False)
     | -- SeasonalityBlock(units=128, thetas_dim=8, backcast_length=50, forecast_length=10, share_thetas=True) at @4779952616
     | -- SeasonalityBlock(units=128, thetas_dim=8, backcast_length=50, forecast_length=10, share_thetas=True) at @4779952952
     | -- SeasonalityBlock(units=128, thetas_dim=8, backcast_length=50, forecast_length=10, share_thetas=True) at @4779953288

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