A novel non-parametric method for time series classification based on k-Nearest Neighbors and Dynamic Time Warping Barycenter Averaging. (February 2019) - Tran, Tuan Minh; Le, Xuan-May Thi; Nguyen, Hien T.; Huynh, Van-Nam
Abstract: Time series classification is one of the most important issues in time series data mining. This problem has attracted more and more attention of researchers in recent years. Among proposed methods in literature, 1-Nearest Neighbor (1-NN), its variants and improvements have been widely considered as hard to be beaten on classification of time series. In this paper, we propose a novel non-parametric method to classify time series. The proposed method, namely Weighted Local Dynamic Time Warping Barycenter Averaging k -Nearest Neighbors (WLDBA k -NN), is an improvement of Local Mean-based k -Nearest Neighbors (LM k -NN) algorithm. It improves LM k -NN in that it replaces the local mean vectors by local Dynamic Time Warping Barycenter (DBA) vectors calculated using our method, namely Weighted DBA (WDBA). By experiments, we show that (i) WLDBA k -NN outperforms the Weighted Local Mean-based k -Nearest Neighbors (WLM k -NN) algorithm, and (ii) both WLM k -NN and WLDBA k -NN outperform 1-NN, LM k -NN, k -Nearest Centroid Neighbors ( k -NCN), and LM k -NCN in 85 time series datasets of UCR Time Series Classification Archive. The experimental results also show that new local mean vectors used in WLM k -NN and WLDBA k -NN significantly contribute to the improvement of the performance of time series classification.