Discriminative extraction of features from time series

Abstract

Abstract A primary challenge of time series classification is how to extract powerful features from training samples. Two kinds of classification methods, global-based and local-based methods, have been studied widely in recent years. The global-based methods, like 1-Nearest Neighbor(1-NN), take the entire series as features, which have the drawback that they are not able to indicate the intrinsic characters of a class. The local-based methods overcome this weakness by employing discriminative time series subsequences as features, called shapelets. However, most local-based methods are computationally expensive because of the massive number of shapelet candidates. In this paper, we propose a novel shapelets extraction method which takes each time series as a high-dimensional data and then finds the discriminative dimensions corresponding to the positions of shapelets. More specifically, the discriminative dimensions are determined by combining Local Fisher Discriminant Analysis (LFDA) method and two sparse restrictions which can encourage the continuous characteristic of time series. Extensive experimental results show that the proposed method achieves significant improvement compared to the existing shapelet-based methods in terms of classification accuracy and running time on the commonly used time series datasets. In addition, comparing with the accepted time series classification methods, NNDTW and COTE, our method still gets better results.