Multivariate Time Series Data Clustering Method Based on Dynamic Time Warping and Affinity Propagation

Xiaoji Wan, Hailin Li, Liping Zhang, Yenchun Jim Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In view of the importance of various components and asynchronous shapes of multivariate time series, a clustering method based on dynamic time warping and affinity propagation is proposed. From the two perspectives of the global and local properties information of multivariate time series, the relationship between the data objects is described. It uses dynamic time warping to measure the similarity between original time series data and obtain the similarity between the corresponding components. Moreover, it also uses the affinity propagation to cluster based on the similarity matrices and, respectively, establishes the correlation matrices for various components and the whole information of multivariate time series. In addition, we further put forward the synthetical correlation matrix to better reflect the relationship between multivariate time series data. Again the affinity propagation algorithm is applied to clustering the synthetical correlation matrix, which realizes the clustering analysis of the original multivariate time series data. Numerical experimental results demonstrate that the efficiency of the proposed method is superior to the traditional ones.

Original languageEnglish
Article number9915315
JournalWireless Communications and Mobile Computing
Publication statusPublished - 2021

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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