mlsanityData to 14 Oct 2026

A Survey on Time-Series Distance Measures

arXiv 2412.20574 ↗First posted 29 Dec 2024cs.DB cs.AI cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 202 references that are in the map. First posted on arXiv 29 Dec 2024.

Builds on

23 of 202 references.

References · 23

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11 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Localized Weather Prediction Using Kolmogorov-Arnold Network-Based Models and Deep RNNscited by 1
  2. 2026Cell Phantom Video Generation in Elliptical Fourier Descriptor Domaincited by 0
  3. 2026READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosiscited by 0