WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
Abstract
Deep learning models often fail to generalize well under distribution shifts. Understanding and overcoming these failures have led to a new research field on Out-of-Distribution (OOD) generalization. Despite being extensively studied for static computer vision tasks, OOD generalization has been severely underexplored for time series tasks. To shine a light on this gap, we present WOODS: 10 challenging time series benchmarks covering a diverse range of data modalities, such as videos, brain recordings, and smart device sensory signals. We revise the existing OOD generalization algorithms for time series tasks and evaluate them using our systematic framework. Our experiments show a large room for improvement for empirical risk minimization and OOD generalization algorithms on our datasets, thus underscoring the new challenges posed by time series tasks.
Cite
Text
Gagnon-Audet et al. "WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series." Transactions on Machine Learning Research, 2023.Markdown
[Gagnon-Audet et al. "WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series." Transactions on Machine Learning Research, 2023.](https://mlanthology.org/tmlr/2023/gagnonaudet2023tmlr-woods/)BibTeX
@article{gagnonaudet2023tmlr-woods,
title = {{WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series}},
author = {Gagnon-Audet, Jean-Christophe and Ahuja, Kartik and Bayazi, Mohammad Javad Darvishi and Mousavi, Pooneh and Dumas, Guillaume and Rish, Irina},
journal = {Transactions on Machine Learning Research},
year = {2023},
url = {https://mlanthology.org/tmlr/2023/gagnonaudet2023tmlr-woods/}
}