title: Visibility Graph Based Time Series Analysis.
abstract: Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.
authors.affiliation: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.|Computer Science Department, Masinde Muliro University of Science and Technology, P.O. Box 190-50100, Kakamega, Kenya.
authors.forename: Mutua
authors.initials: M
authors.lastname: Stephen
authors.affiliation: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.
authors.forename: Changgui
authors.initials: C
authors.lastname: Gu
authors.affiliation: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.
authors.forename: Huijie
authors.initials: H
authors.lastname: Yang
mesh_terms: D008962:Models, Theoretical; D016011:Normal Distribution; D013997:Time Factors