Time Series data Mining Using the Matrix Profile part 1
Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 1
Authors:
Abdullah Al Mueen, Department of Computer Science, University of New Mexico
Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside
Abstract:
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
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/
Видео Time Series data Mining Using the Matrix Profile part 1 канала KDD2017 video
Authors:
Abdullah Al Mueen, Department of Computer Science, University of New Mexico
Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside
Abstract:
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
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/
Видео Time Series data Mining Using the Matrix Profile part 1 канала KDD2017 video
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