Regression Methods for Forecasting the State of Telecommunication Networks

Telecommunication networks play a critical role in the smooth data flow in today's digital world, making their steady operation essential. Because of the increased dependence on these networks, sophisticated monitoring and management systems are required to ensure continuous online connection and data sharing.This article aims to investigate the use of quantile and logical regression in predicting the performance of telecommunication networks. It seeks to build a prediction model based on real-time data to aid intelligent decision-making systems in network management.The study employs machine learning methods to design an intelligent control system based on the autocorrelation of time-based variables. This system combines a communications network, a database of system attributes, a machine learning-based data processing system, and a decision-making system.The intelligent system demonstrates the telecom network's real-time monitoring, analysis, and management capabilities. Its goal is to provide telecommunications operators with an efficient predictive model for improving network performance and resource allocation while addressing complex network dynamics, diverse data sources, forecasting accuracy, real-time decision-making, and model resilience.Constructing a trustworthy mathematical model using regression techniques and machine learning dramatically improves the forecasting of telecommunication network conditions. This innovation is critical in allowing proactive management techniques and ensuring end-users get high-quality services.