Analysis and visualization of complex phenomena

In this paper we study natural and man-made complex phenomena by means of signal processing and fractional calculus. The outputs of the complex phenomena are time series to be interpreted as manifestations of the system dynamics. In a first step we use the Jensen-Shannon divergence to compare real-world signals. We then adopt multidimensional scaling and visualization tools for data clustering and analysis. Classical and generalized (fractional) Jensen-Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand distinct phenomena.