Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations
文献类型：期刊论文
| 作者 | Zhang, Liying2,3; Pei, Tao1,3; Meng, Bin4; Lian, Yuanfeng2; Jin, Zhou2 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2020 |
| 卷号 | 8页码:167998-168007 |
| 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 |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2020.3022625 |
| 通讯作者 | Pei, Tao(peit@Ireis.ac.cn) |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China[41525004] ; National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[41877523] ; National Natural Science Foundation of China[41671165] ; State Key Laboratory of Resources and Environmental Information System ; Science Foundation of China University of Petroleum, Beijing[ZX20200100] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000572962900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Information System ; Science Foundation of China University of Petroleum, Beijing |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156955]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Pei, Tao |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.China Univ Petr, Coll Informat Sci & Engn, Beijing 102249, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Liying,Pei, Tao,Meng, Bin,et al. Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations[J]. IEEE ACCESS,2020,8:167998-168007. |
| APA | Zhang, Liying,Pei, Tao,Meng, Bin,Lian, Yuanfeng,&Jin, Zhou.(2020).Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations.IEEE ACCESS,8,167998-168007. |
| MLA | Zhang, Liying,et al."Two-Phase Multivariate Time Series Clustering to Classify Urban Rail Transit Stations".IEEE ACCESS 8(2020):167998-168007. |
入库方式： OAI收割
来源：地理科学与资源研究所
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