Matrix Profile XIX: Time Series Semantic Motifs: A New Primitive for Finding HigherLevel Structure in Time Series Time series motifs are approximately repeated patterns in realvalued temporal data. They are used for exploratory data mining methods including clustering, classification, segmentation, and rule discovery. Their current definition is limited to finding literal or nearexact matches and is unable to discover higher level semantic structure. Consider a time series generated by an accelerometer on a smartwatch. This data offers the possibility of finding motifs in human behavior. One such example is the motif generated by a handshake. Under current motif definitions, a singlepump handshake would not match a threepump handshake, even though they are culturally and semantically equivalent events. In this work we generalize the definition of motifs to one which allows us to capture higher level semantic structure. We refer to these as time series semantic motifs. Surprisingly this increased expressiveness does not come at a great cost. Our algorithm SemanticMotifFinder takes approximately the same time as current stateoftheart motif discovery algorithms. Furthermore, we demonstrate the utility of our ideas on diverse datasets.

Matrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets 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.