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Parsimonious Linear Fingerprinting for Time Series

Summary: PLiF discovers essential fingerprints via joint dynamics in time series, yielding interpretable, compact features. Linear in sequence length; supports clustering, compression, visualization, forecasting, and segmentation with strong real-data gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10284
Venue
VLDB
Year
2010
Pagerank
5.093636e-05
Overall Rank
12,471 | 14.44%
DOI
10.14778/1920841.1920893

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@article{li_vldb10,
        title = {{Parsimonious Linear Fingerprinting for Time Series}},
        author = {Li, Lei and Prakash, B. Aditya and Faloutsos, Christos},
        journal = {PVLDB},
        series = {{VLDB} '10},
        volume = {3},
        number = {1},
        pages = {385--396},
        doi = {10.14778/1920841.1920893},
        url = {https://doi.org/10.14778/1920841.1920893},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
4,379 AutoPlait: Automatic Mining of Co-evolving Time Sequences 2014 SIGMOD 6.735265e-05
4,424 Classical and Contemporary Approaches to Big Time Series Forecasting 2019 SIGMOD 6.7104881e-05
5,158 Forecasting Big Time Series: Old and New 2018 VLDB 6.3404905e-05
10,069 Mining and Forecasting of Big Time-series Data 2015 SIGMOD 5.1643809e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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