Foundations of Sequence-to-Sequence Modeling for Time Series

Zelda Mariet, Vitaly Kuznetsov
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:408-417, 2019.

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 this Paper

BibTeX
@InProceedings{pmlr-v89-mariet19a, title = {Foundations of Sequence-to-Sequence Modeling for Time Series}, author = {Mariet, Zelda and Kuznetsov, Vitaly}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {408--417}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/mariet19a/mariet19a.pdf}, url = {https://proceedings.mlr.press/v89/mariet19a.html}, 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.} }
Endnote
%0 Conference Paper %T Foundations of Sequence-to-Sequence Modeling for Time Series %A Zelda Mariet %A Vitaly Kuznetsov %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-mariet19a %I PMLR %P 408--417 %U https://proceedings.mlr.press/v89/mariet19a.html %V 89 %X 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.
APA
Mariet, Z. & Kuznetsov, V.. (2019). Foundations of Sequence-to-Sequence Modeling for Time Series. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:408-417 Available from https://proceedings.mlr.press/v89/mariet19a.html.

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