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ISSD: Indicator Selection for Time Series State Detection

Summary: ISSD frames indicator selection as upstream optimization for time-series state detection. Channel set completeness/quality from segment-level sampling drives a Pareto-front NP-hard approximation to a compact indicator subset; validated on multiple datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hc2d170557f531666
Venue
SIGMOD
Year
2025
Pagerank
5.1708619e-05
Overall Rank
9,476 | 36.29%
DOI
10.1145/3709698

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod25,
        title = {{ISSD: Indicator Selection for Time Series State Detection}},
        author = {Wang, Chengyu and Zhou, Tongqing and Chen, Lin and Zhao, Shan and Cai, Zhiping},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3709698},
        url = {https://dl.acm.org/doi/10.1145/3709698},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,054 CLaP - State Detection from Time Series 2026 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3,434 Finding Semantics in Time Series 2011 SIGMOD 7.3027739e-05
4,473 AutoPlait: Automatic Mining of Co-evolving Time Sequences 2014 SIGMOD 6.5841437e-05
7,559 Raising the ClaSS of Streaming Time Series Segmentation 2024 VLDB 5.4964346e-05
9,489 Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data 2023 SIGMOD 5.1708619e-05
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