Format

Send to

Choose Destination
PLoS One. 2015 Nov 16;10(11):e0143015. doi: 10.1371/journal.pone.0143015. eCollection 2015.

Visibility Graph Based Time Series Analysis.

Author information

1
Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.
2
Computer Science Department, Masinde Muliro University of Science and Technology, P.O. Box 190-50100, Kakamega, Kenya.

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.

PMID:
26571115
PMCID:
PMC4646626
DOI:
10.1371/journal.pone.0143015
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center