Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering
Summary: Time2Feat: end-to-end MTS clustering that extracts inter- and intra-signal interpretable features and applies dimensionality reduction to yield compact, human-understandable representations. Supports few-shot semi-supervision to steer cluster selection and boost accuracy. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,274 | Interpretable Clustering of Multivariate Time Series with Time2Feat | 2023 | VLDB | 4.7748043e-05 |
| 10,391 | In-Database Time Series Clustering | 2025 | SIGMOD | 4.1905499e-05 |
| 10,609 | Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching | 2025 | VLDB | 4.1905499e-05 |
| 10,888 | Representative Time Series Discovery for Data Exploration | 2025 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,510 | k-Shape: Efficient and Accurate Clustering of Time Series | 2015 | SIGMOD | 0.00011588558 |
| 8,187 | FeatTS: Feature-based Time Series Clustering | 2021 | SIGMOD | 4.5606638e-05 |
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