Complex Network Analysis of Nonlinear Time Series

Dear Colleague,

In the last two decades, the analysis and testing of time series models has led to the development of a number of complex methodologies. Time series contain data of mostly a nonstationary nature and with chaotic, dynamic, and nonlinear characteristics. The largest number of time series models is applied from various financial data, but also data from many other areas (for example, engineering science, medicine, COVID-19 studies, physics, social science, and more). Consequently, classical regression models often cannot efficiently analyze complex and dynamical networks of such data.

Numerous studies in the literature have confirmed this statement and offer complex models with strong links to nonlinear dynamics. The most used models are machine learning methods (neural network, support vector regression, long short-term memory, Markov switching models, regression tree, gradient boosting, Bayesian sequential estimation, random forest, and more) and different threshold and regression models (dynamic panel threshold regression model, smooth transition model, Fourier ADF unit root test, etc.). The significance of these models is even greater because most authors use them for predictions of time series data (especially machine learning methods).

The goal of this Special Issue is the interpretation and theoretical and practical implication of existing approaches of complex network analysis of nonlinear time series. The Special Issue will also consider manuscripts that analyze models of time series forecasting, with emphasis on recent developments. Manuscripts should be focused on theoretical-methodological analysis, practical application of complex network methods, and/or future insights of complex nonlinear models.

Dr. Darko Vukovic
Guest Editor

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