梳理100+时间序列距离度量方法，助力高效分析与应用
A Survey on Time-Series Distance Measures
- 按7类框架系统分类超100种度量方法
- 覆盖单变量与多变量场景，对比适用差异
- 适合研究时间序列分析的学者与工程师
距离度量是时间序列分析任务（如查询、索引、分类、聚类、异常检测和相似性搜索）的核心基础。随着各领域时间序列数据的快速增长，评估这些度量方法的有效性与效率愈发重要。本文综述了超过100种前沿距离度量方法，分为7类：固定对齐、滑动窗口、弹性匹配、核函数、基于特征、基于模型及嵌入表示。不仅提供完整的数学框架，还深入探讨各类方法在单变量与多变量场景下的区别与应用场景。通过全面归纳与洞察，为未来创新的时间序列距离度量发展奠定基础。
原文摘要 · Abstract (English)
Distance measures have been recognized as one of the fundamental building blocks in time-series analysis tasks, e.g., querying, indexing, classification, clustering, anomaly detection, and similarity search. The vast proliferation of time-series data across a wide range of fields has increased the relevance of evaluating the effectiveness and efficiency of these distance measures. To provide a comprehensive view of this field, this work considers over 100 state-of-the-art distance measures, classified into 7 categories: lock-step measures, sliding measures, elastic measures, kernel measures, feature-based measures, model-based measures, and embedding measures. Beyond providing comprehensive mathematical frameworks, this work also delves into the distinctions and applications across these categories for both univariate and multivariate cases. By providing comprehensive collections and insights, this study paves the way for the future development of innovative time-series distance measures.
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