Matrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets
Kaveh Kamgar, Shaghayegh Gharghabi, Eamonn Keogh · 2019
In recent years the data mining community has largely coalesced around the idea that many problems in time series analytics essentially reduce to finding and then reasoning about repeated structure in time series. Existing tools can find conserved structure within a single time series (motifs) and between pairs of time series (joins). However, to date there are no tools to find repeated structure in sets of time series, an idea we call time series consensus motifs in recognition of their similarity to their discrete analogs in DNA strings. In this work we introduce a definition of time series consensus motifs and a scalable algorithm to discover them in large data collections. We further show that given this new primitive, we can solve multiple higherlevel problems in time series data mining. We demonstrate the utility of our ideas with case studies in diverse domains.