A Structured Study of Multivariate Time-Series Distance Measures
Summary: Evaluation of multivariate time-series distances: 30 measures, 8 categories, 13 normalizations, 30 datasets, 3 tasks. Findings: non-Z normalizations beat Z-score; elastic measures outperform DTW/Euclidean; sliding measures balance accuracy and runtime. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jens E. d'Hondt (Eindhoven University of Technology)
- 2. Haojun Li (Ohio State University)
- 3. Fan Yang (Ohio State University)
- 4. Odysseas Papapetrou (Eindhoven University of Technology)
- 5. John Paparrizos (Ohio State University)
BibTeX Citation
@inproceedings{dhondt_sigmod25,
title = {{A Structured Study of Multivariate Time-Series Distance Measures}},
author = {d'Hondt, Jens E. and Li, Haojun and Yang, Fan and Papapetrou, Odysseas and Paparrizos, John},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725258},
url = {https://dl.acm.org/doi/10.1145/3725258},
year = {2025}
}
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