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

机译：时间序列的新颖距离度量：最大移动相关距离

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摘要

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.
机译：时间序列距离或相似性度量是时间序列数据挖掘中最重要的问题之一，包括表示，聚类，分类和离群值检测。现有的距离量度可能无法有效地处理序列中在相位和幅度上的漂移。在这项研究中，提出了一种新的测量方法，即最大移动相关距离（MSCD），以提高时间序列距离测量的准确性和效率。通过对曲线配准和相关性进行积分，可以分别消除相位和幅度的失准或漂移。另外，具有“收缩效应”的MSCD的第二距离（MSCD-2nd）增强了簇内样本的相似性。第二距离对动态时间扭曲（DTW）距离具有相同的影响。实验结果表明，就时间序列聚类和分类的准确性和效率而言，MSCD-2nd是首选方法。 （C）2018 Elsevier B.V.保留所有权利。

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