Abstract
The existing pattern matching methods of multivariate time series can hardly measure the similarity of multivariate hydrological time series accurately and efficiently. Considering the characteristics of multivariate hydrological time series, the continuity and global features of variables, we proposed a pattern matching method, PP-DTW, which is based on dynamic time warping. In this method, the multivariate time series is firstly segmented, and the average of each segment is used as the feature. Then, PCA is operated on the feature sequence. Finally, the weighted DTW distance is used as the measure of similarity in sequences. Carrying out experiments on the hydrological data of Chu River, we conclude that the pattern matching method can effectively describe the overall characteristics of the multivariate time series, which has a good matching effect on the multivariate hydrological time series.
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Acknowledgement
This work is supported by (1) National Natural Science Foundation of China (61300122); (2) A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions; (3) Water Science and Technology Project of Jiangsu Province (2013025).
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Gai, Z., Lou, Y., Ye, F., Li, L. (2017). Research on Pattern Matching Method of Multivariate Hydrological Time Series. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_6
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DOI: https://doi.org/10.1007/978-981-10-6385-5_6
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