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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• complex network
• nonlinear models
• dynamic regression
• financial time series management
• polynomial function forecasting
• learning machine
• kernel-based models
• nonlinear models of time series forecasting
• LSTM, SVM, GMDH, MSA, auto-encoder networks, recurrent neural network
• network optimization
• multilayer perceptions
• exponential smoothing
• newbolt/granger grouping scheme
• auto-encoder networks
• MSA bivariate copula model
• chaotic, dynamic, and nonlinear time series data management
• threshold regression models
• applicative testing of complex nonlinear models in COVID-19 studies, physics, crisis events and different social science cases
• theoretical insights of complex nonlinear methodologies