Researching Algorithms Used to Forecast Sales Volumes of Telecommunications Equipment

This article presents a study of the problems of forecasting sales volumes of telecommunications equipment. The objective of the work is to determine the most effective algorithm used for forecasting sales volumes of various types of telecommunications equipment. The relevance of this work lies in the rapid growth of information and data transmitted by telecommunications equipment, as well as the need for IT companies to keep up with the times and be able to promptly adjust business strategies for the purchase and sale of such equipment. Traditional methods based on probability theory and mathematical statistics cannot effectively generate forecasts given the above trends in global business processes. The application of machine learning methods focused on forecasting this type of product can significantly improve the efficiency of solving this problem. The object of the study is sales volumes of telecommunications equipment. The subject of the research is metrics for assessing the quality of algorithms and machine learning models built on them, designed to forecast sales volumes of telecommunications equipment. The article draws conclusions on the practical application of each algorithm and indicates areas for promising research.