Models for study of the influence of statistical characteristics of computer networks traffic on the efficiency of prediction by machine learning tools

The article is an attempt to streamline and categorize a huge stream of publications on modern methods, techniques, and models of data forecasting of various nature in terms of their applicability for traffic forecasting in computer networks. The specified ordering is performed within the framework of the proposed conceptual model of forecasting algorithms. Within the framework of this conceptual model, the characteristics of both computer network traffic models and traffic control methods that can be explicitly or implicitly used in modern prediction software tools are highlighted. It is shown that the analysis of such probabilistic aspects of data description as the presence of significant nonstationarity, some nonlinear effects in data models, as well as the specifics of data distribution laws can influence the efficiency of learning predictors.