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)
Incoming Non-self Citations Over Time
Authors
- 1. Chengyu Wang (National University of Defense Technology)
- 2. Tongqing Zhou (National University of Defense Technology)
- 3. Lin Chen (National University of Defense Technology)
- 4. Shan Zhao (Hefei University of Technology)
- 5. Zhiping Cai (National University of Defense Technology)
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 |
Previous
Page 1 / 1
Next
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 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,872 | Clean4TSDB: A Data Cleaning Tool for Time Series Databases | 2024 | VLDB |
| 2 | 10,760 | KDSelector: A Framework of Knowledge-Enhanced and Data-Efficient Selector Learning for Anomaly Detection Model Selection in Time Series | 2026 | VLDB |
| 3 | 6,073 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB |
| 4 | 9,204 | Fast and Adaptive Indexing of Multi-Dimensional Observational Data | 2016 | VLDB |
| 5 | 8,382 | Mining Deviants in a Time Series Database | 1999 | VLDB |
| 6 | 6,383 | Characterizing and Selecting Fresh Data Sources | 2014 | SIGMOD |
| 7 | 7,559 | Raising the ClaSS of Streaming Time Series Segmentation | 2024 | VLDB |
| 8 | 7,640 | KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection | 2025 | SIGMOD |
| 9 | 11,054 | CLaP - State Detection from Time Series | 2026 | VLDB |
| 10 | 9,489 | Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data | 2023 | SIGMOD |