TSDS-Toolbox：時間序列資料集相似性度量工具箱

摘要

人工智慧的快速發展顯著加速了時間序列分析的研究，特別是在預測、分類和生成任務方面。近期模型，尤其是基礎模型，受益於時間序列資料集之間的相似性，因為該相似性在微調的來源資料集選擇中扮演關鍵角色。然而，許多現有的時間序列資料集相似性方法基準評測實作分散且難以擴展。為了解決此問題，我們提出了一個統一框架，即時間序列資料集相似性工具箱（TSDS-Toolbox）。我們的工作實現了：(1) 對時間序列資料集相似性方法進行系統性且可重現的比較；(2) 提供靈活的可擴展性，讓使用者能新增自定義資料集、相似性方法及下游時間序列任務；以及 (3) 透過整合的時間序列資料集縮減器，對資料集層級與序列層級的相似性方法進行一致的評估。TSDS-Toolbox 的有效性已透過多樣實驗設定下的全面實驗得到驗證。我們的工具箱已公開提供。

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.