Recent advances in bridging time series and complex networks

This paper reviews the recent advances in bridging time series analysis and complex network theory. We first introduce the main representative approaches of mapping time series into complex networks with their potential and advantages in applications in a variety of fields, and also report the latest methods to feature complex networks with typical time series after multi-scaling transformation, and to estimate and identify the uncertain connectivity of a complex network. As an example, we show an amplitude-temporal method to fast detect arrhythmia with the indicator of the average degree of associated networks.