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
- 2. Tongqing Zhou
- 3. Lin Chen
- 4. Shan Zhao
- 5. Zhiping Cai
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,321 | CLaP - State Detection from Time Series | 2026 | VLDB | 4.1905499e-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 |
|---|---|---|---|---|
| 2,684 | Finding Semantics in Time Series | 2011 | SIGMOD | 8.3154143e-05 |
| 4,066 | AutoPlait: Automatic Mining of Co-evolving Time Sequences | 2014 | SIGMOD | 6.4756873e-05 |
| 6,797 | Raising the ClaSS of Streaming Time Series Segmentation | 2024 | VLDB | 4.9194315e-05 |
| 9,156 | Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data | 2023 | SIGMOD | 4.380727e-05 |
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| 10,321 | CLaP - State Detection from Time Series | 2026 | VLDB | 4.1905499e-05 |
| 9,156 | Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data | 2023 | SIGMOD | 4.380727e-05 |