Models for Studying the Impact of Statistics Characteristics of Computing Network Gridlock on the Effectiveness of Predictions using Machine Learning - Al-janabi, Adel (The Indonesian Journal of Computer Science, 2025)
The article endeavors to organize and classify a vast array of papers about contemporary approaches, strategies, and methods of data forecast across many domains, specifically with their usefulness in traffic forecast in networks of computers. The defined ordering is carried out within the context of the suggested concept model of forecast algorithms. This concept model emphasizes the qualities of both computational network activity models and traffic monitoring approaches that may be employed openly or implicitly in contemporary forecast software applications. It is demonstrated that the investigation of probabilistic characteristics for data definition, such as presence of considerable nonstationarity, certain nonlinear impacts in models of data, and the uniqueness of dissemination of data laws, could impact effectiveness in learning predictions.