Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures
Summary: Comprehensive re-evaluation of time-series distance measures; debunks four long-standing misconceptions. Assesses 71 measures over 128 datasets with 8 normalizations, 52 lock-step, 4 sliding, 7 elastic, 4 kernel, 4 embedding, plus rigorous statistics. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. John Paparrizos (University of Chicago)
- 2. Chunwei Liu (University of Chicago)
- 3. Aaron J. Elmore (University of Chicago)
- 4. Michael J. Franklin (University of Chicago)
BibTeX Citation
@inproceedings{paparrizos_sigmod20,
title = {{Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures}},
author = {Paparrizos, John and Liu, Chunwei and Elmore, Aaron J. and Franklin, Michael J.},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389760},
url = {https://dl.acm.org/doi/10.1145/3318464.3389760},
year = {2020}
}
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