Finding recurrence networks' threshold adaptively for a specific time series

Abstract

Recurrence-plot-based recurrence networks are an approach used to analyze time series using a complex networks theory. In both approaches-recurrence plots and recurrence networks-, a threshold to identify recurrent states is required. The selection of the threshold is important in order to avoid bias of the recurrence network results. In this paper, we propose a novel method to choose a recurrence threshold adaptively. We show a comparison between the constant threshold and adaptive threshold cases to study period-chaos and even period-period transitions in the dynamics of a prototypical model system. This novel method is then used to identify climate transitions from a lake sediment record.