A novel distance measure for time series: Maximum shifting correlation distance

Time series distance or similarity measurement is one of the most important problems in time series data mining, including representation, clustering, classification, and outlier detection. The existing distance measures may not efficiently deal with the drifts in the series in both the phase and amplitude. In this study, a novel measurement, maximum shifting correlation distance (MSCD), is proposed to improve the accuracy and efficiency of the time series distance measure. By integrating the curve registration and correlation, the misalignments or drifts in the phase and amplitude can be eliminated, respectively. In addition, the second distance of the MSCD (MSCD-2nd), which has the "shrinkage effect", enhances the similarity of the samples within a cluster. The second distance has the same effect on the dynamic time warping (DTW) distance. The experimental results demonstrate that MSCD-2nd is the preferred measure in terms of the accuracy and efficiency for time series clustering and classification. (C) 2018 Elsevier B.V. All rights reserved.