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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Lei Li (Carnegie Mellon University)
- 2. B. Aditya Prakash (Carnegie Mellon University)
- 3. Christos Faloutsos (Carnegie Mellon University)
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)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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