Analysis and forecasting of modern telecommunication systems traffic based on artificial intelligence methods

Abstract

In recent years, artificial intelligence technologies have demonstrated significant success in solving the problem of traffic analysis and forecasting in various telecommunication systems. Forecasting allows the telecom operator to know about the future behavior of the network, take timely necessary measures to improve the quality of customer service, and decide on the need to install or upgrade equipment. Using data collected from IoT mobile devices as an example, this article provides an overview and analysis of various time series forecasting models describing the traffic behavior of telecommunication systems. Forecasting models such as the exponential smoothing method, linear regression, the autoregressive integrated moving average (ARIMA) method, the support vector machine regression method, the N-BEATS method, which uses fully connected layers of a neural network for forecasting a one-dimensional time series, are discussed; the features of some of them are briefly outlined. For a specific data array, data preparation operations are described: removing unused columns, replacing missing data on transaction durations with their median values, and describing the main statistical characteristics of the data array. A preliminary data analysis is presented, which consists of using smoothing methods: moving average and exponential smoothing. The process of training models and a comparative analysis of the quality of their training are described. For this data set, it was concluded that for the UDP protocol the ARIMA model has the best learning quality, for the TCP protocol - linear regression and the Theta model, for the HTTPS protocol – linear regression, ARIMA and N-BEATS.