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The most useful data mining primitives are distance measures. With aneffective distance measure, it is possible to perform classification,clustering, anomaly detection, segmentation, etc. For single-event time seriesEuclidean Distance and Dynamic Time Warping distance are known to be extremelyeffective. However, for time series containing cyclical behaviors, the semanticmeaningfulness of such comparisons is less clear. For example, on two separatedays the telemetry from an athlete workout routine might be very similar. Thesecond day may change the order in of performing push-ups and squats, addingrepetitions of pull-ups, or completely omitting dumbbell curls. Any of theseminor changes would defeat existing time series distance measures. Somebag-of-features methods have been proposed to address this problem, but weargue that in many cases, similarity is intimately tied to the shapes ofsubsequences within these longer time series. In such cases, summative featureswill lack discrimination ability. In this work we introduce PRCIS, which standsfor Pattern Representation Comparison in Series. PRCIS is a distance measurefor long time series, which exploits recent progress in our ability tosummarize time series with dictionaries. We will demonstrate the utility of ourideas on diverse tasks and datasets.

Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series