Efficient Time Series Classification via Sparse Linear Combination

Abstract

Time series classification presents a specific machine learning challenge due to the ordering of variables. Recent studies show that the simple nearest neighbor classifier with elastic distance measures is hard to beat and many researchers focus on alternative distance measures. Unlike nearest neighbor classifier try to find a training sample which has the minimum distance with test instance, we utilize a reconstruction strategy to determine the label of new time series in this paper. Concretely, for each test time series, we reconstruct it by using as few training samples as possible and then calculate the residuals between the test time series and the selected training samples of each class. The test time series is classified to the class with minimum residual. To get the required time series from the training set, we employ sparse restriction technique to discover the optimal combination of different training samples while fitting test time series. Meanwhile, to solve the scenarios where the time series dataset is linearly inseparable, we extend our method by the kernel trick. Extensive experimental results show that the proposed method can gain the significant improvement on commonly used time series datasets.