Deep autoregressive models with spectral attention

Fernando Moreno-Pino, Pablo M. Olmos, Antonio Artés-Rodríguez

Abstract: Highlights•We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, merging global and local frequency domain information in the model’s embedded space.•Two spectral attention models, global and local to the time series, integrate global trends and seasonality patterns within the forecast and perform spectral filtering to remove time series’s noise.•The proposed Spectral Attention module, responsible of all frequency domain operations, can be easily incorporated into well-known forecast frameworks.•Experiments unveil how Spectral Attention stands out, consistently out-performing the base models it is integrated into.