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Finding Approximately Repeated Patterns in Time Series
This is the diP’s fourth distinguished lecture, delivered this time by Eamonn Keogh, professor and Ross Family Chair in the Department of Computer Science and Engineering at University of California, Riverside. More information and material is available on our website: https://u-paris.fr/diip/diip-seminars/
Abstract
Time series data mining is the task of finding patterns, regularities, and outliers in massive datasets. Given the ubiquity of time series in medicine, science, and industry, time series data mining is of increasing importance. In this talk I shall argue that the simple primitive of time series motif discovery, the task of finding approximately repeated patterns with a dataset, is the most useful core operation in all of time series data mining. In particular, it can be used as a primitive to enable many other useful tasks, such as summarization, segmentation, classification, clustering and anomaly detection. I will argue my case with examples of motif discovery in datasets as diverse as penguin behavior, cardiology, and astronomy.
Short Bio
Eamonn Keogh is a distinguished professor and Ross Family Chair in the Department of Computer Science and Engineering. He specializes in time series data mining, finding patterns, regularities, and outliers in massive datasets. He developed some of the most commonly used definitions, algorithms and data representations used in this area. These contributions include SAX, PAA, Time Series Shapelets, Time Series Motifs, the LBkeogh lower bound, and the Matrix Profile. These ideas have been used by thousands of academic, industrial, and scientific researchers worldwide, including NASA’s Jet Propulsion Laboratory, which uses Keogh’s ideas to find anomalies in observations of the magnetosphere collected by the Cassini spacecraft in orbit around Saturn. In the week following this talk, he will be presented with the 2021 IEEE ICDM Research Contributions Award.