Data mining to predict failures of communication network devices

Abstract

The complexity of telecommunication systems (TCS) and the configuration of communication networks, as well as a large set of data for assessing the state of devices, represent the main vector for the development and improvement of control systems for such communication networks. With the development of information and software technologies, another problem has become the reasonable choice of an apparatus that, taking into account its intellectualization and the characteristics of the initial data set, would correctly calculate and demonstrate the sensitivity of the result to external and internal changes. Modern methods of data processing make it possible to carry out a preventive analysis of the future functioning of TCS devices with flexible adjustment of dependent factors. The choice of one or another method will largely affect the result of the assessment, including its adequacy. And the forecast should also be estimated by this property with a high degree of accuracy. Not every method of data analysis is suitable for a particular data set. So the paper compares the most popular methods of data mining (from autoregression to neural networks) with a conclusion about the compliance with the criteria of working and predicted data. The authors also set out a verbal way of interpreting the predicted data using the example of a sample of failures.