TripleShapelet Networks for Time Series Classification Shapelets are discriminative subsequences for time series classification (TSC). Although shapeletbased methods have achieved good performance and interpretability, they still have two issues that can be improved. First, previous methods only assess a shapelet by how accurately it can classify all the samples. However, for multiclass imbalanced classification tasks, these methods will ignore the shapelets that can distinguish minority class from other classes and will tend to use the shapelets that are useful for discriminating the majority classes. Second, the shapelets are fixed after the training phase and cannot adapt to time series with deformations, which will lead to poor matches to the shapelets. In this paper, we propose a novel endtoend shapelet learning model called Triple Shapelet Networks (TSNs) to extract multilevel feature representations. Specifically, TSN learns the most discriminative shapelets by gradient descent similar to previous methods. In addition, it learns categoryspecific shapelets for each class by using auxiliary binary classifiers. Finally, it uses a shapelet generator to produce samplespecific shapelets conditioned on subsequences of the input time series. The addition of categorylevel and samplelevel shapelets to the standard model improves the performance. Experiments conducted on extensive time series data sets show that TSN is stateoftheart compared to existing shapeletbased methods, and the visualization analysis also shows its effectiveness.

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.