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Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases

Summary: APCA yields per-series, locally optimized segments of varying lengths for high-fidelity reconstruction. Index APCA on a multidimensional index with LB and a tight non-LB distance, enabling fast exact and approximate searches. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3327
Venue
SIGMOD
Year
2001
Pagerank
0.00026105472
Overall Rank
190 | 98.70%
DOI
10.1145/375663.375680

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{keogh_sigmod01,
        title = {{Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases}},
        author = {Keogh, Eamonn and Chakrabarti, Kaushik and Mehrotra, Sharad and Pazzani, Michael},
        series = {{SIGMOD} '01},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/375663.375680},
        url = {https://dl.acm.org/doi/10.1145/375663.375680},
        year = {2001}
}

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