  The availability of large amounts of time series data, paired with the
performance of deep-learning algorithms on a broad class of problems, has
recently led to significant interest in the use of sequence-to-sequence models
for time series forecasting. We provide the first theoretical analysis of this
time series forecasting framework. We include a comparison of
sequence-to-sequence modeling to classical time series models, and as such our
theory can serve as a quantitative guide for practitioners choosing between
different modeling methodologies.
