Foundations of Sequence-to-Sequence Modeling for Time Series
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:408-417, 2019.
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.