| title | TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity |
|---|---|
| authors | Yen-Ku Liu, Hongjie Chen, Ryan A. Rossi, Franck Dernoncourt |
| date | 2026-08-08 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.08119 |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | The rapid advancement of artificial intelligence (AI) has significantly accelerated research in time-series analysis, particularly in forecasting, classification, and generation tasks. Recent models, especially foundation models, benefit from time-series dataset similarity due to its significant role in source dataset selection for fine-tuning. However, many existing implementations for benchmarking time-series dataset similarity methods are fragmented and difficult to extend. To address this, we present a unified framework, the Time-Series Dataset Similarity Toolbox (TSDS-Toolbox). Our work enables (1) systematic and reproducible comparisons of time-series dataset similarity methods; (2) flexible extensibility for users to add customized datasets, similarity methods, and downstream time-series tasks; and (3) consistent evaluation of both dataset-level and series-level similarity methods through integrated time-series dataset reducers. The effectiveness of TSDS-Toolbox is validated through comprehensive experiments under diverse experimental settings. Our toolbox is publicly available. |
cite
@misc{liu2026tsds,
title={TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity},
author={Yen-Ku Liu and Hongjie Chen and Ryan A. Rossi and Franck Dernoncourt},
year={2026},
eprint={2608.08119},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.08119}
}