TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity
Yen-Ku Liu, Hongjie Chen, Ryan A. Rossi, Franck Dernoncourt
cs.LG
Submitted: 2026-08-08
Updated: 2026-08-11
Code: https://github.com/yenkuliu/TSDS-Toolbox
License: http://creativecommons.org/licenses/by/4.0/
The gist: The rapid advancement of artificial intelligence (AI) has significantly accelerated research in time-series analysis, particularly in forecasting, classification, and generation tasks.
Terminology
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.
Sources
- Chronos: Learning the Language of Time Series
- The UEA multivariate time series classification archive, 2018
- Measuring Time-Series Dataset Similarity using Wasserstein Distance
- A decoder-only foundation model for time-series forecasting
- The impact of data set similarity and diversity on transfer learning success in time series forecasting
- Retrieval Augmented Time Series Forecasting
- sktime: A Unified Interface for Machine Learning with Time Series
- ClimaX: A foundation model for weather and climate
- TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
- Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
- Quantifying Data Similarity Using Cross Learning
- TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
- Estimating Time Series Foundation Model Transferability via In-Context Learning
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