Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes
Summary: Introduces SDTS, a scalable, parameter-free method for time-series prediction under weak, regional, noisy labels, variable-duration patterns, and severe class imbalance. Demonstrates competitiveness with expert-tuned, domain-specific methods on million-point datasets. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chin-Chia Michael Yeh (University of California Riverside)
- 2. Nickolas Kavantzas (Oracle)
- 3. Eamonn Keogh (University of California Riverside)
BibTeX Citation
@article{yeh_vldb17,
title = {{Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes}},
author = {Yeh, Chin-Chia Michael and Kavantzas, Nickolas and Keogh, Eamonn},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {12},
pages = {1802--1815},
doi = {10.14778/3137765.3137806},
url = {https://doi.org/10.14778/3137765.3137806},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,545 | Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series | 2020 | VLDB | 8.4401333e-05 |
| 6,959 | Time Series Data Mining: A Unifying View | 2023 | VLDB | 5.7303405e-05 |
| 10,828 | TELESAFE: Detecting Private/Work Boundary Crossings in Energy Consumption Trails in Telework | 2025 | VLDB | 5.093636e-05 |
| 11,350 | Weakly Guided Adaptation for Robust Time Series Forecasting | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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