Detecting Time Series Periodicity Using Complex Networks

Abstract

Complex networks are a unified form of representing complex systems. Through this representation is possible to study dynamical systems and make time series analysis using network techniques. One common characteristic of many real world time series is the periodicity. Detecting these periods is interesting because it permits to forecast the series behaviour. Some techniques have been proposed to search for these periods but many of them fail to deal with noisy data. In this paper, we present an algorithm for periodicity detection in noisy data based on community detection. First, the method transforms a time series by dividing it into intervals that are represented by vertices. Then, we apply community detection in order to cluster highly connected ranges. These clusters of vertices represent periodic changes in the series and can be used to detect periodicity. The efficiency of the proposed method is illustrated in a meteorological case study where we detected periodicity in a noisy temperature data.