Telecommunications Trouble Ticket Resolution Time Modelling with Machine Learning

Abstract: This report explores whether machine learning methods such as regression and classification can be used with the goal of estimating the resolution time of trouble tickets in a telecommunications network. Historical trouble ticket data from Telenor were used to train different machine learning models. Three different machine learning classifiers were built: a support vector classifier, a logistic regression classifier and a deep neural network classifier. Three different machine learning regressors were also built: a support vector regressor, a gradient boosted trees regressor and a deep neural network regressor. The results from the different models were compared to determine what machine learning models were suitable for the problem. The most important features for estimating the trouble ticket resolution time were also investigated. Two different prediction scenarios were investigated in this report. The first scenario uses the information available at the time of ticket creation to make a prediction. The second scenario uses the information available after it has been decided whether a technician will be sent to the affected site or not. The conclusion of the work is that it is easier to make a better resolution time estimation in the second scenario compared to the first scenario. The differences in results between the different machine learning models were small. Future work can include more information and data about the underlying root cause of the trouble tickets, more weather data and power outage information in order to make better predictions. A standardised way of recording and logging ticket data is proposed to make a future trouble ticket time estimation easier and reduce the problem of missing data.