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Optimal Multi-scale Patterns in Time Series Streams

Summary: Multi-scale pattern discovery in time-series streams: learns local patterns across window sizes with a criterion to capture oscillatory/aperiodic trends. Novelty: learn a data-driven orthonormal transform; no fixed bases, enabling fast incremental streaming with order-of-magnitude savings. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3851
Venue
SIGMOD
Year
2006
Pagerank
8.5779023e-05
Overall Rank
2,441 | 83.26%
DOI
10.1145/1142473.1142545

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{papadimitriou_sigmod06,
        title = {{Optimal Multi-scale Patterns in Time Series Streams}},
        author = {Papadimitriou, Spiros and Yu, Philip S.},
        series = {{SIGMOD} '06},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1142473.1142545},
        url = {https://dl.acm.org/doi/10.1145/1142473.1142545},
        year = {2006}
}

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