Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering

Summary: Time2Feat clusters multivariate time series using interpretable inter- and intra-signal features, then selects a compact informative subset. Optional expert supervision further improves accuracy and interpretability while reducing feature complexity. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: h4aa0c44cba5b4ff9
Venue: VLDB
Year: 2023
Pagerank: 6.8701633e-05
Overall Rank: 3,990 | 73.18%
DOI: 10.14778/3565816.3565822

Incoming Non-self Citations Over Time

Authors

1. Angela Bonifati (Claude Bernard Lyon 1 University; National Centre for Scientific Research)
2. Francesco Del Buono (University of Modena and Reggio Emilia)
3. Francesco Guerra (University of Modena and Reggio Emilia)
4. Donato Tiano (Claude Bernard Lyon 1 University; National Centre for Scientific Research)

BibTeX Citation

@article{bonifati_vldb23,
        title = {{Time2Feat: Learning Interpretable Representations for Multivariate Time Series Clustering}},
        author = {Bonifati, Angela and Del Buono, Francesco and Guerra, Francesco and Tiano, Donato},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {2},
        pages = {193--201},
        doi = {10.14778/3565816.3565822},
        url = {https://doi.org/10.14778/3565816.3565822},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,151	Interpretable Clustering of Multivariate Time Series with Time2Feat	2023	VLDB	5.5982871e-05
10,483	MUFASA: Fast and Accurate Multivariate Time-Series Clustering	2026	SIGMOD	4.9793485e-05
11,007	GALACTICA: An Interactive System for Local and Global Counterfactual Explanations for Time-series Clustering	2026	VLDB	4.9793485e-05
11,108	In-Database Time Series Clustering	2025	SIGMOD	4.9793485e-05
11,264	Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching	2025	VLDB	4.9793485e-05
11,457	Representative Time Series Discovery for Data Exploration	2025	VLDB	4.9793485e-05

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
1,579	k-Shape: Efficient and Accurate Clustering of Time Series	2015	SIGMOD	0.00010186397
8,201	FeatTS: Feature-based Time Series Clustering	2021	SIGMOD	5.3787337e-05

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