Learning Location-Guided Time-Series Shapelets

Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima

Abstract: Shapelets are interclass discriminative subsequences that can be used to characterize target classes. Learning shapelets by continuous optimization has recently been studied to improve classification accuracy. However, there are two issues in previous studies. First, since the locations where shapelets appear in the time series are determined by only their shapes, shapelets may appear at incorrect and non-discriminative locations in the time series, degrading the accuracy and interpretability. Second, the theoretical interpretation of learned shapelets has been limited to binary classification. To tackle the first issue, we propose a continuous optimization that learns not only shapelets but also their probable locations in a time series, and we show theoretically that this enhances feature discriminability. To tackle the second issue, we provide a theoretical interpretation of shapelet closeness to the time series for target / off-target classes when learning with softmax loss, which allows for multi-class classification. We demonstrate the effectiveness of the proposed method in terms of accuracy, runtime, and interpretability on the UCR archive.