Temporal classification for fault-prediction in a real-world telecommunications network

This paper presents a new temporal classification approach for fault-prediction in a telecommunications network. The countrywide data network of Pakistan Telecom (PTCL) has been selected as a basis for the investigation of classification algorithms to predict faults before they stop a large number of users´ circuits from normal operation. The main problems addressed are the evaluation of alarms and development of new machine learning tools to help overcome the interoperability issues. The motivation behind this work is to assist human operators and minimize the cost of the alarm evaluation process.