Back to papers
CLaP - State Detection from Time Series
Summary: CLaP reframes unsupervised state detection by self-supervising a TS classifier: cross-validating classifiers on segment-labelled subsequences to quantify confusion and merge segments into latent states. Outperforms six SOTA on 405 TS with higher precision and a superior accuracy–runtime tradeoff; scalable with a Python implementation.
(summarized by gpt-5-mini on Mar 13 2026)
- Paper ID
- 14366
- Venue
- VLDB
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,321 | 28.27%
- DOI
-
10.14778/3772181.3772187
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 33 |
BIRCH: An Efficient Data Clustering Method for Very Large Databases |
1996 |
SIGMOD |
0.00077399244 |
| 211 |
Gorilla: A Fast, Scalable, In-Memory Time Series Database |
2015 |
VLDB |
0.0003401421 |
| 263 |
OPTICS: Ordering Points To Identify the Clustering Structure |
1999 |
SIGMOD |
0.00029955858 |
| 1,510 |
k-Shape: Efficient and Accurate Clustering of Time Series |
2015 |
SIGMOD |
0.00011588558 |
| 1,925 |
Apache IoTDB: Time-series Database for Internet of Things |
2020 |
VLDB |
0.00010073156 |
| 2,070 |
Chimp: Efficient Lossless Floating Point Compression for Time Series Databases |
2022 |
VLDB |
9.6331032e-05 |
| 4,066 |
AutoPlait: Automatic Mining of Co-evolving Time Sequences |
2014 |
SIGMOD |
6.4756873e-05 |
| 5,747 |
Hercules Against Data Series Similarity Search |
2022 |
VLDB |
5.3427166e-05 |
| 6,688 |
Motiflets - Simple and Accurate Detection of Motifs in Time Series |
2023 |
VLDB |
4.9575975e-05 |
| 6,797 |
Raising the ClaSS of Streaming Time Series Segmentation |
2024 |
VLDB |
4.9194315e-05 |
| 7,274 |
Interpretable Clustering of Multivariate Time Series with Time2Feat |
2023 |
VLDB |
4.7748043e-05 |
| 9,147 |
ISSD: Indicator Selection for Time Series State Detection |
2025 |
SIGMOD |
4.380727e-05 |
| 9,156 |
Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data |
2023 |
SIGMOD |
4.380727e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 5,479 |
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles |
2022 |
VLDB |
5.4849266e-05 |
| 6,435 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0555305e-05 |
| 4,066 |
AutoPlait: Automatic Mining of Co-evolving Time Sequences |
2014 |
SIGMOD |
6.4756873e-05 |
| 9,147 |
ISSD: Indicator Selection for Time Series State Detection |
2025 |
SIGMOD |
4.380727e-05 |
| 13,170 |
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning |
2024 |
VLDB |
- |
| 4,258 |
Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes |
2017 |
VLDB |
6.3041152e-05 |
| 6,419 |
AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data |
2024 |
VLDB |
5.0621949e-05 |
| 9,559 |
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis |
2024 |
VLDB |
4.3212967e-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 |