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Method of Time Series Similarity Measurement Based on Dynamic Time Warping

Lianggui Liu1,*, Wei Li1, Huiling Jia1
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
* Corresponding Author: Lianggui Liu. Email: .

Computers, Materials & Continua 2018, 57(1), 97-106. https://doi.org/10.32604/cmc.2018.03511

Abstract

With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the combination of dynamic time warping and derivative dynamic time warping is proposed. The new method considers not only the distance between time series, but also the shape characteristics of time series. We demonstrated that our method can outperform DTW and DDTW through extensive experiments with respect to cophenetic correlation.

Keywords

Time series, PCA dimensionality reduction, dynamic time warping, hierarchical clustering, cophenetic correlation.

Cite This Article

L. Liu, W. Li and H. Jia, "Method of time series similarity measurement based on dynamic time warping," Computers, Materials & Continua, vol. 57, no.1, pp. 97–106, 2018.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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