Hybrid SARIMA-ELM-based Data Traffic Forecasting: The Case of UMTS Network in Addis Ababa, Ethiopia

In Universal Mobile Telecommunications Service (UMTS) network planning, data traffic demand is one critical input in deciding dimension of network elements. Past data collected from deployed UMTS network can be used to forecast future demand. In the context of ethio telecom, the sole telecom service provider in Ethiopia, the future demand forecast is, however, based on number of subscribers growth forecast obtained from marketing section. This approach assumes uniform data demand per subscriber to obtain the total data demand. Understandably, it does not utilize the data growth information which is already available in the network.
Forecasting the traffic demand based on historical data from network can enhance the marketing inputs and the traffic model accuracy. In this regard, taking data from ethio telecom’s UMTS network, a prior research has used Seasonal Autoregressive Integrated Moving Average (SARIMA) model to forecast a one month data traffic demand. However, the research did not consider the non-linearity observed in the data traffic. This thesis handles this non-linearity via a hybrid model that accounts the linearity with SARIMA model and the non-linearity via Extreme Learning Machine (ELM) model; here after called the hybrid SARIMA-ELM model.