基于时间加权改进的 LDTW 算法.

DTW is one of the commonly used algorithms in time series similarity measurement. However, DTW has the shortcoming of pathological alignment and ignores the influence of time attribute. LDTW and TDTW have been proposed to handle two shortcomings of DTW separately, however they cannot be solved simultaneously by LDTW or TDTW independently. This paper proposed TLDTW algorithm. Firstly, it constructed time weight matrix by measuring the distance between points in two series. Secondly, it fused the corresponding time weights from time weight matrix into the recursive filling procedure for cumulative cost matrix of LDTW, thus it considered the time attribute and the problem of pathological alignment could still be suppressed. It conducted 1-NN classification experiment based on UCR dataset, and experimental results show that the classification accuracy based on TLDTW is better than other compared algorithms, and the reliability of TLDTW is verified by further comparison. [ABSTRACT FROM AUTHOR]