  Time series forecasting is an important problem across many domains, playing
a crucial role in multiple real-world applications. In this paper, we propose a
forecasting architecture that combines deep autoregressive models with a
Spectral Attention (SA) module, which merges global and local frequency domain
information in the model's embedded space. By characterizing in the spectral
domain the embedding of the time series as occurrences of a random process, our
method can identify global trends and seasonality patterns. Two spectral
attention models, global and local to the time series, integrate this
information within the forecast and perform spectral filtering to remove time
series's noise. The proposed architecture has a number of useful properties: it
can be effectively incorporated into well-know forecast architectures,
requiring a low number of parameters and producing interpretable results that
improve forecasting accuracy. We test the Spectral Attention Autoregressive
Model (SAAM) on several well-know forecast datasets, consistently demonstrating
that our model compares favorably to state-of-the-art approaches.
