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TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity

arXiv 2608.08119 ↗First posted 8 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

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.

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19 of 45 references.

References · 19

  1. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  2. 2024Chronos: Learning the Language of Time Seriescited by 759
  3. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
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  5. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 614
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  7. 2018The UEA multivariate time series classification archive, 2018cited by 338
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  10. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 197
  11. 2020Geometric Dataset Distances via Optimal Transportcited by 158
  12. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 144
7 more
  1. 2019sktime: A Unified Interface for Machine Learning with Time Seriescited by 102
  2. 2018Transfer learning for time series classificationcited by 60
  3. 2024Retrieval Augmented Time Series Forecastingcited by 46
  4. 2023Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignmentcited by 5
  5. 2024The impact of data set similarity and diversity on transfer learning success in time series forecastingcited by 3
  6. 2025Estimating Time Series Foundation Model Transferability via In-Context Learningcited by 2
  7. 2025Measuring Time-Series Dataset Similarity using Wasserstein Distancecited by 1

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