Seasonal Auto-Regression Integrated Moving Average-based Data Traffic Forecasting: The Case of UMTS Network in Addis Ababa, Ethiopia
| dc.contributor.advisor | Hailemariam, Dereje (PhD) | |
| dc.contributor.author | Salem, Amel | |
| dc.date.accessioned | 2018-06-22T07:52:23Z | |
| dc.date.accessioned | 2023-11-04T15:14:34Z | |
| dc.date.available | 2018-06-22T07:52:23Z | |
| dc.date.available | 2023-11-04T15:14:34Z | |
| dc.date.issued | 2016-02 | |
| dc.description.abstract | In planning, operating and developing mobile data networks, one crucial input is the telecommunication demand that includes number of subscribers and their required service data rates. These numbers should be predicted accurately for optimal planning and to capture the needs of the subscriber thereby creating customer satisfaction. Ethio-telecom, the sole telecom service provider in Ethiopia, has recently introduced different service charging systems that include flat rate and package-based data services. This has increased the number of subscribers who are using these services, which in turn has led to a substantial increase in data traffic, and hence, a burden on the existing infrastructure. Such increases in demand should be considered in planning phases, where proper forecasting of the data demand growth is one integral input for the planning. Based on the available information, the current data growth forecast practice being employed by Ethio-telecom is mainly based on marketing information. This thesis presents Seasonal Auto-Regression Integrated Moving Average (SARIMA) model as an alternative way of forecasting Universal Mobile Telecommunication System (UMTS) mobile data traffic taking the city of Addis Ababa as a case study. The approach in this thesis involves investigating the past UMTS data traffic load collected from the core network to find an appropriate model which describes the inherent structure of the UMTS data-traffic and forecast the future data traffic load. With this forecasting model, it is observed that the expected monthly data traffic per user for smart phones can reach up to 7GB as compared to the current 1GB cap. As the first practice (to the best of our knowledge) for data forecasting using available data in Ethio-telecom, it is hoped that the approach shown here will be useful for subsequent infrastructure expansion planning in a way that guarantees better customer satisfaction. Key Words: ACF, forecasting, PACF, SARIMA, Seasonality, Trend, UMTS, UMTS Datatraffic. Page | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/2893 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | ACF | en_US |
| dc.subject | Forecasting | en_US |
| dc.subject | PACF | en_US |
| dc.subject | Sarima | en_US |
| dc.subject | Seasonality | en_US |
| dc.subject | Trend | en_US |
| dc.subject | Umts Datatraffic | en_US |
| dc.title | Seasonal Auto-Regression Integrated Moving Average-based Data Traffic Forecasting: The Case of UMTS Network in Addis Ababa, Ethiopia | en_US |
| dc.type | Thesis | en_US |