N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
This work addresses time series forecasting for domains like finance and tourism, offering an interpretable and fast-to-train model, though it is incremental as it builds on existing deep learning primitives.
The authors tackled univariate time series forecasting by proposing N-BEATS, a deep neural architecture based on residual links and fully-connected layers, achieving state-of-the-art performance with an 11% accuracy improvement over statistical benchmarks and 3% over the previous M4 competition winner.
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.