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Investigating the influence of seasonality and structural breakpoint on telecommunication network traffic prediction

Mobile network traffic forecasting enhances the adequate use of available resources and promote bandwidth allocation to mobile service users. This study is proposing a combination of first order difference and periodogram to determine the existence of seasonal pattern in 3G downlink traffic. In addition, the study seeks to identify the existence of structural break and also the exact day the structural break occur in the 3G downlink traffic data using Perron methodology. The study have established that there is no presence of seasonality pattern in the 3G downlink traffic data used for analysis. The results also indicated that, even though there is existence of breaks on day 622 at intercept level, on day 599 at trend level and on day 396 for both intercept and trend level there is no influence on the prediction of 3G downlink traffic. The study also proved that ARIMA (0,1,2) can be used to predict 3G downlink traffic with no influence of structural break and seasonality.