Time Series Forecasting (7): N-BEATS — Interpretable Deep Architecture

Sat, 30 Nov 2024 09:00:00 +0000

The 2018 M4 forecasting competition served 100,000 series across six frequencies as a single benchmark. The leaderboard was dominated by hand-tuned ensembles built from decades of statistical-forecasting craft. Then a pure neural network with no statistical preprocessing, no feature engineering, and no recurrence won outright. That network was N-BEATS by Oreshkin et al. — a stack of fully-connected blocks with two residual paths. Its interpretable variant additionally split the forecast into a polynomial trend and a Fourier seasonality, so the very thing classical statisticians wanted (a readable decomposition) came for free.

N-BEATS on Chen Kai Blog

Time Series Forecasting (7): N-BEATS — Interpretable Deep Architecture