Time Series Data Mining: A Unifying View
Summary: A unifying tutorial argues that MASS and the Matrix Profile, combined with minimal supporting code, cover 90–99.9% of common time-series mining tasks. Demonstrates this claim across diverse real-world domains with reproducible datasets and code. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Eamonn Keogh (University of California Riverside)
BibTeX Citation
@article{keogh_vldb23,
title = {{Time Series Data Mining: A Unifying View}},
author = {Keogh, Eamonn},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3861--3863},
doi = {10.14778/3611540.3611570},
url = {https://doi.org/10.14778/3611540.3611570},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,738 | TD-Join: Leveraging Temporal Dependencies in Time Series Joins | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,029 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00012557065 |
| 4,496 | Sim-Piece: Highly Accurate Piecewise Linear Approximation through Similar Segment Merging | 2023 | VLDB | 6.6622617e-05 |
| 4,684 | Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes | 2017 | VLDB | 6.5630679e-05 |
| 5,266 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 6.2939621e-05 |
| 6,912 | SENSOR: Data-driven Construction of Sketch-based Visual Query Interfaces for Time Series Data | 2022 | VLDB | 5.7409522e-05 |
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