Change Detection in Telecommunication Data using Time Series Analysis and Statistical Hypothesis Testing

Abstract: In the base station system of the GSM mobile network there are a large number of counters tracking the behaviour of the system. When the software of the system is updated, we wish to find out which of the counters that have changed their behaviour. This thesis work has shown that the counter data can be modelled as a stochastic time series with a daily profile and a noise term. The change detection can be done by estimating the daily profile and the variance of the noise term and perform statistical hypothesis tests of whether the mean value and/or the daily profile of the counter data before and after the software update can be considered equal. When the chosen counter data has been analysed, it seems to be reasonable in most cases to assume that the noise terms are approximately independent and normally distributed, which justies the hypothesis tests. When the change detection is tested on data where the software is unchanged and on data with known software updates, the results are as expected in most cases. Thus the method seems to be applicable under the conditions studied.