TSDS-Toolbox：一种用于度量时间序列数据集相似性的工具箱

摘要

人工智能（AI）的快速发展显著加速了时间序列分析的研究，尤其是在预测、分类和生成任务方面。最近的模型，尤其是基础模型，受益于时间序列数据集的相似性，因为这种相似性在微调的源数据集选择中起着重要作用。然而，许多现有的用于基准测试时间序列数据集相似性方法的实现是碎片化的且难以扩展。为了解决这一问题，我们提出了一个统一框架——时间序列数据集相似性工具箱（TSDS-Toolbox）。我们的工作实现了：（1）对时间序列数据集相似性方法进行系统且可复现的比较；（2）用户可灵活扩展以添加自定义数据集、相似性方法和下游时间序列任务；（3）通过集成的时间序列数据集归约器，对数据集级别和序列级别的相似性方法进行一致的评估。TSDS-Toolbox的有效性通过在不同实验设置下的综合实验得到了验证。我们的工具箱已公开可用。

English

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.