文献类型：期刊文献

英文题名：Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations

作者：Zhang, Liying[1,2];Pei, Tao[2,3];Meng, Bin[4];Lian, Yuanfeng[1];Jin, Zhou[1]

第一作者：Zhang, Liying

通讯作者：Pei, T[1];Pei, T[2]

机构：[1]China Univ Petr, Coll Informat Sci & Engn, Beijing 102249, Peoples R China;[2]Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;[3]Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China;[4]Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China

第一机构：China Univ Petr, Coll Informat Sci & Engn, Beijing 102249, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;[2]corresponding author), Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.

年份：2020

卷号：8

起止页码：167998-168007

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20211210115728);Scopus(收录号：2-s2.0-85102757542);WOS:【SCI-EXPANDED(收录号:WOS:000572962900001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 41525004, Grant 41421001, Grant 41877523, and Grant 41671165; in part by the State Key Laboratory of Resources and Environmental Information System; and in part by the Science Foundation of China University of Petroleum, Beijing, under Grant ZX20200100.

语种：英文

外文关键词：Time series analysis; Correlation; Shape; Correlation coefficient; Discrete wavelet transforms; Clustering algorithms; Multivariate time series; cluster; maximum overlap discrete wavelet transform; symbolic aggregate approximation (SAX); urban rail transit stations

摘要：Consider the problem of clustering objects with temporally changing multivariate variables, for instance, in the classification of cities with several changing socioeconomic indices in geographical research. If the changing multivariate can be recorded simultaneously as a multivariate time series, in which the length of each subseries is equal and the subseries can be correlated, the problem is transformed into a multivariate time series clustering problem. The available methods consider the correlations between distinct time series but overlook the shape of each time series, which causes multivariate time series with similar correlations and opposite shapes to be clustered into the same class. To overcome this problem, this paper proposes a two-phase multivariate time series clustering algorithm that considers both correlation and shape. In Phase I, the discrete wavelet transform is applied to capture the wavelet variances and the correlation coefficients between each pair of variables to realize the initial clustering of multivariate time series, where time series with a similar correlation but opposite shape may be assigned to the same cluster. In Phase II, multivariate time series are clustered based on shape via the symbolic aggregate approximation (SAX) method. In this phase, time series with similar correlations but opposite morphologies are differentiated. The method is evaluated using multivariate time series of incoming and outgoing passenger volumes from Beijing IC card data; these volume data were collected between March 4, 2013 and March 17, 2013. Based on the silhouette coefficient, our approach outperforms two popular multivariate time series clustering methods: a wavelet-based method and the SAX method.