Foundations of Sequence-to-Sequence Modeling for Time Series
Abstract
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.
Cite
Text
Mariet and Kuznetsov. "Foundations of Sequence-to-Sequence Modeling for Time Series." Artificial Intelligence and Statistics, 2019.Markdown
[Mariet and Kuznetsov. "Foundations of Sequence-to-Sequence Modeling for Time Series." Artificial Intelligence and Statistics, 2019.](https://mlanthology.org/aistats/2019/mariet2019aistats-foundations/)BibTeX
@inproceedings{mariet2019aistats-foundations,
title = {{Foundations of Sequence-to-Sequence Modeling for Time Series}},
author = {Mariet, Zelda and Kuznetsov, Vitaly},
booktitle = {Artificial Intelligence and Statistics},
year = {2019},
pages = {408-417},
volume = {89},
url = {https://mlanthology.org/aistats/2019/mariet2019aistats-foundations/}
}