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

2026-08-08

AI Summary

This work addresses the lack of a unified and reproducible evaluation framework for time series dataset similarity measures, which has been hindered by fragmented implementations and limited extensibility. To overcome these limitations, the authors propose a modular and extensible toolbox that, for the first time, integrates both dataset-level and sequence-level similarity metrics within a single framework. The proposed system supports customizable data inputs, algorithms, and downstream tasks through a flexible architecture comprising time series dimensionality reducers, diverse similarity measurement algorithms, and plug-in task interfaces. Comprehensive experiments demonstrate the framework’s effectiveness and generalizability across multiple settings. The open-sourced implementation significantly enhances the reproducibility and adaptability of similarity evaluation in time series analysis.

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.

Problem

• time-series dataset similarity
• benchmarking
• foundation models
• reproducibility
• extensibility

Innovation

• time-series dataset similarity
• foundation models
• reproducible benchmarking
• extensible framework
• dataset reduction