Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes

Summary: Matrix Profile IV introduces SDTS, a domain-agnostic, parameter-free method for learning from weakly labeled time series with variable-length patterns. Rivals expert-tuned domain-specific methods in neuroscience and entomology. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11462
Venue: VLDB
Year: 2017
Pagerank: 6.6647073e-05
Overall Rank: 4,621 | 67.89%
DOI: -

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,497	Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series	2020	VLDB	8.5707162e-05
6,836	Time Series Data Mining: A Unifying View	2023	VLDB	5.8190903e-05
10,570	TELESAFE: Detecting Private/Work Boundary Crossings in Energy Consumption Trails in Telework	2025	VLDB	5.1725247e-05
11,147	Weakly Guided Adaptation for Robust Time Series Forecasting	2024	VLDB	5.1725247e-05

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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