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

Code

• github.com/unit8co/darts
  pytorch★ 9,275
• github.com/jdb78/pytorch-forecasting
  pytorch★ 4,841
• github.com/Nixtla/neuralforecast
  pytorch★ 4,010
• github.com/philipperemy/n-beats
  pytorch★ 901
• github.com/etna-team/etna
  pytorch★ 193
• github.com/weifantt/depts
  pytorch★ 41
• github.com/DFrolova/ml-sk-project
  pytorch★ 23
• github.com/Nixtla/nixtlats
  none★ 12
• github.com/Chasm4359/ProTS
  pytorch★ 10
• github.com/potosnakw/neuralforecast
  pytorch★ 3

Abstract

We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.

Tasks

Reproductions