Matrix Profile XIX: Time Series Semantic Motifs: A New Primitive for Finding Higher-Level Structure in Time Series

Time series motifs are approximately repeated patterns in real-valued 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 near-exact 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 single-pump handshake would not match a three-pump 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 Semantic-Motif-Finder takes approximately the same time as current state-of-the-art motif discovery algorithms. Furthermore, we demonstrate the utility of our ideas on diverse datasets.