发表机构
National Yang Ming Chiao Tung University; Dolby Laboratories; Adobe Research（国立阳明交通大学; 杜比实验室; 奥多比研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有时间序列数据集相似度基准实现零散难扩展的问题，提出TSDS-Toolbox工具箱，支持系统比较、灵活扩展与一致评估，经实验验证有效且公开可用。
AI 中文摘要
人工智能（AI）的快速发展极大地推动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。近期的模型，特别是基础模型，从时间序列数据集相似度中受益，因为它在微调的源数据集选择中发挥着重要作用。然而，许多现有的用于基准测试时间序列数据集相似度方法的实现是零散的，难以扩展。为解决这一问题，我们提出了一个统一框架——时间序列数据集相似度工具箱（TSDS-Toolbox）。该工作支持：（1）对时间序列数据集相似度方法进行系统且可复现的比较；（2）灵活的可扩展性，允许用户添加自定义数据集、相似度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和序列级相似度方法进行一致的评估。TSDS-Toolbox的有效性通过在多种实验设置下的综合实验得到验证，该工具箱已公开可用。
英文摘要
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.
