Directed vector visibility graph from multivariate time series: a new method to measure time series irreversibility

As a practical tool, visibility graph provides a different perspective to characterize time series. In this paper, we present a new visibility algorithm called directed vector visibility graph and combine it with the Kullback-Leibler divergence to measure the irreversibility of multivariable time series. T directed vector visibility algorithm converts the time series into a directed network. Subsequently, the ingoing and outgoing degree distributions of the directed network can be got to calculate the Kullback-Leibler divergence, which will be applied to assess the level of irreversibility of the time series. This is a simple and effective method without any special symbolic process. The numerical results from various types of systems are used to validate that this method can accurately distinguish reversible time series from those irreversible ones. Finally, we employ this method to estimate the irreversibility of financial time series and the results show that our method is efficient to analyze the financial time series irreversibility.

Automated summary

The paper introduces a new visibility algorithm for time series, which combines with Kullback-Leibler divergence to measure the irreversibility of multivariable time series. This method is simple and effective, accurately distinguishing reversible and irreversible time series, with successful application in analyzing financial time series irreversibility.