Foundations of Sequence-to-Sequence Modeling for Time Series

作者：Vitaly Kuznetsov, Zelda Mariet · 发表于：arXiv (Cornell University) · 年份：2018 · DOI：10.48550/arxiv.1805.03714 · 被引用次数：25 · 研究领域：Time Series Analysis and Forecasting、Stock Market Forecasting Methods、Forecasting Techniques and Applications

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.