Early classification of time series based on trend segmentation and optimization cost function

The two objectives of early classification, accuracy and earliness, contradict with each other. In order to solve the problems of poor interpretation, huge candidate set of shapelets and adjustable quantification between the two objectives, a novel method of early classification of time series based on trend segmentation and optimization of cost function is proposed. Latent information of time series is mined by trend segmentation, and time stamp of discriminative shapelets is extracted. The number of shapelet candidates is greatly reduced by pruning based on the length and location, which improved the discrimination capability of chosen shapelets. An adjustable objective function is also defined to make a trade-off between accuracy and earliness, and then realize the early classification of time series. In view of the earliness and accuracy problems of different tendencies, this paper defines different coefficients to adjust the optimization objective function. The experimental results on UCR repository show that our proposed method achieves competitive results both at earliness and accuracy.