Regularized shapelet learning for scalable time series classification

Abstract

Time series shapelets are subsequences that best split time series data into classes. Therefore, shapelet discovery has attracted considerable interest in the time series classification community. However, almost all state-of-the-art shapelet-based time series classification methods have an inevitably high computational cost. To overcome this drawback, we present a regularized shapelet learning framework in which the fused lasso regularizer is used to maintain the time order of the shapelets and different loss functions can be employed to improve the speed and accuracy of the time series classification. The proposed framework converts the traditional brute force shapelet searching process into a regularized machine learning problem. The most prominent advantage of this conversion is that the speed of the shapelet learning process and the discrimination of the learned shapelets are both theoretically guaranteed. As such, both the speed and accuracy of the shapelet-based time series classification are improved in this paper. Comparison experiments on several datasets show that our framework effectively reduces the training time of time series classification while improving the classification accuracy.