scholarly journals Research on dynamic time warping multivariate time series similarity matching based on shape feature and inclination angle

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Danyang Cao ◽  
Jie Liu
Keyword(s):  
Time Series ◽  
Inclination Angle ◽  
Dynamic Time Warping ◽  
Multivariate Time Series ◽  
Shape Feature ◽  
Time Warping ◽  
Similarity Matching ◽  
Dynamic Time

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

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoji Wan ◽  
Hailin Li ◽  
Liping Zhang ◽  
Yenchun Jim Wu
Keyword(s):  
Time Series ◽  
Correlation Matrix ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Affinity Propagation ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time ◽  
The Relationship

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.

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