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

No Thumbnail Available



Journal Title

Journal ISSN

Volume Title




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. A one and half year (i.e., from April 2015 – June 2016) data traffic collected from five Radio Network Controllers (RNCs) of the UMTS network in the city of Addis Ababa is used for the forecast. The forecasting performance metrics are: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Square Error (MASE). The results indicate that the hybrid SARIMA-ELM model with SARIMA order of (0,1,1) (1,0,1)7 is selected with 3.75% increase in forecast than SARIMA only model. The outperform SARIMA-MLP, which has the second lower error, with 24.8% percentage error reduction.



Time Series, traffic, prediction, model, forecast, linear, non-linearity, hybrid, residual SARIMA model, UMTS, ELM model