KDD 2017 Tutorials

Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins

author: Abdullah Al Mueen, Department of Computer Science, University of New Mexico
author: Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside
published: Nov. 21, 2017, recorded: August 2017, views: 1729

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Description

The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc.

Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html

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