Raising the ClaSS of Streaming Time Series Segmentation
Summary: ClaSS: a streaming time-series segmentation method that evaluates partition homogeneity using self-supervised time-series classification and statistical tests to detect significant change points. Complexity independent of segment sizes (linear in window), beats 8 baselines, available as a Flink window operator (~1k pts/s). (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Arik Ermshaus (Humboldt University of Berlin)
- 2. Patrick Schäfer (Humboldt University of Berlin)
- 3. Ulf Leser (Humboldt University of Berlin)
BibTeX Citation
@article{ermshaus_vldb24,
title = {{Raising the ClaSS of Streaming Time Series Segmentation}},
author = {Ermshaus, Arik and Schäfer, Patrick and Leser, Ulf},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {8},
pages = {1953--1966},
doi = {10.14778/3659437.3659450},
url = {https://doi.org/10.14778/3659437.3659450},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,118 | Discovering Leitmotifs in Multidimensional Time Series | 2025 | VLDB | 5.580601e-05 |
| 9,516 | ISSD: Indicator Selection for Time Series State Detection | 2025 | SIGMOD | 5.151324e-05 |
| 11,124 | CLaP - State Detection from Time Series | 2026 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 147 | Gorilla: A Fast, Scalable, In-Memory Time Series Database | 2015 | VLDB | 0.00028941363 |
| 1,351 | Chimp: Efficient Lossless Floating Point Compression for Time Series Databases | 2022 | VLDB | 0.00010921539 |
| 1,950 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 9.303437e-05 |
| 2,935 | Monarch: Google’s Planet-Scale In-Memory Time Series Database | 2020 | VLDB | 7.8259754e-05 |
| 3,689 | Detecting Change in Data Streams | 2004 | VLDB | 7.078365e-05 |
| 4,496 | AutoPlait: Automatic Mining of Co-evolving Time Sequences | 2014 | SIGMOD | 6.5592659e-05 |
| 5,335 | Hercules Against Data Series Similarity Search | 2022 | VLDB | 6.1613957e-05 |
| 6,934 | Motiflets - Simple and Accurate Detection of Motifs in Time Series | 2023 | VLDB | 5.6248915e-05 |
| 9,086 | NLC: Search Correlated Window Pairs on Long Time Series | 2022 | VLDB | 5.2094282e-05 |
| 9,087 | DiAl: Distributed Streaming Analytics Anywhere, Anytime | 2013 | VLDB | 5.2094282e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
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| 2 | 4,021 | Continually Evaluating Similarity-Based Pattern Queries on a Streaming Time Series | 2002 | SIGMOD |
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| 7 | 12,645 | Space-Efficient Estimation of Statistics over Sub-Sampled Streams | 2012 | PODS |
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| 9 | 8,105 | Data Stream Clustering: An In-depth Empirical Study | 2023 | SIGMOD |
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