Affiliation:
1. School of Economics and Management, Fuzhou University 1 , Fuzhou 350108, China
2. School of Artificial Intelligence, Optics, and Electronics (iOPEN), Northwestern Polytechnical University 2 , Xian 710072, China
3. School of Computer Science, Inner Mongolia University 3 , Inner Mongolia 010021, China
Abstract
In this paper, we propose an efficient segmentation approach in order to divide a multivariate time series through integrating principal component analysis (PCA), visibility graph theory, and community detection algorithm. Based on structural characteristics, we can automatically divide the high-dimensional time series into several stages. First, we adopt the PCA to reduce the dimensions; thus, a low dimensional time series can be obtained. Hence, we can overcome the curse of dimensionality conduct, which is incurred by multidimensional time sequences. Later, the visibility graph theory is applied to handle these multivariate time series, and corresponding networks can be derived accordingly. Then, we propose a community detection algorithm (the obtained communities correspond to the desired segmentation), while modularity Q is adopted as an objective function to find the optimal. As indicated, the segmentation determined by our method is of high accuracy. Compared with the state-of-art models, we find that our proposed model is of a lower time complexity (O(n3)), while the performance of segmentation is much better. At last, we not only applied this model to generated data with known multiple phases but also applied it to a real dataset of oil futures. In both cases, we obtained excellent segmentation results.
Funder
National Natural Science Foundation of China-China Academy of China
National Natural Science Foundation of China
Shaanxi Key Science and Technology Innovation Team Project
Key Research and Development Projects of Shaanxi Province
ministry of education of humanities and social science project
Subject
Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献