There is nowadays a constant flux of data being generated and collected in all types of real
world systems. These data sets are often indexed by time, space, or both requiring …

We review the state of the art of clustering financial time series and the study of their
correlations alongside other interaction networks. The aim of the review is to gather in one …

We propose multilayer information spillover networks, including return spillover layer,
volatility spillover layer, and extreme risk spillover layer in the variance decomposition …

Our understanding of a variety of phenomena in physics, biology and economics crucially
depends on the analysis of multivariate time series. While a wide range tools and …

Financial networks have become extremely useful in characterizing the structures of
complex financial systems. Meanwhile, the time evolution property of the stock markets can …

Stock price prediction is an important and challenging problem for studying financial
markets. Existing studies are mainly based on the time series of stock price or the operation …

The emergence of explosive synchronization transitions in networks of phase oscillators
recently has become one of the most interesting topics. It is widely believed that the large …

High-frequency and unit-level consumption data collected by smart meters makes accurate
and short-term predictions of sectoral electricity demand possible. To facilitate electricity …

The analysis of cross-correlations is extensively applied for the understanding of
interconnections in stock markets and the portfolio risk estimation. Current studies of …

The fast pace of urbanisation may benefit or be detrimental to the socio-economic status of
urban areas. Understanding how the configuration of urban areas influences the socio …