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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
10094
Venue
VLDB
Year
2010
Pagerank
4.1905499e-05
Overall Rank
12,284 | 14.63%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
4,066 AutoPlait: Automatic Mining of Co-evolving Time Sequences 2014 SIGMOD 6.4756873e-05
4,476 Classical and Contemporary Approaches to Big Time Series Forecasting 2019 SIGMOD 6.1458665e-05
4,892 Forecasting Big Time Series: Old and New 2018 VLDB 5.8474831e-05
9,919 Mining and Forecasting of Big Time-series Data 2015 SIGMOD 4.2520778e-05
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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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