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neural basis expansion analysis

Neural basis expansion analysis is a deep learning framework for time series forecasting and sequence modeling that combines multilayer neural networks with functional basis expansions to decompose, reconstruct, and predict sequential signals. In this architecture, deep stacks of fully connected layers use forward and backward residual connections to generate expansion coefficients rather than unconstrained point outputs. These coefficients linearly weight a set of basis functions to produce both a backward reconstruction of the historical input and a forward forecast of future values. The basis functions can be domain-specific mathematical functions, such as polynomial curves to model underlying trends and Fourier harmonic series to model seasonal variations, or generic, fully learnable projections. This formulation enables accurate time series predictions alongside interpretable component decomposition without requiring recurrent units or manual feature engineering.

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

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

Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio

OrganizationsElement AIMila

Why you should read this

Proposes a pure deep learning architecture comprised of backward and forward residual basis expansions that matches classical interpretability without requiring domain-specific feature engineering.

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.

Added

2026-06-27