TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity
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Computer Science > Machine Learning
Title:TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.08119 [cs.LG] |
| (or arXiv:2608.08119v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.08119
arXiv-issued DOI via DataCite (pending registration)
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