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Prediction model for registered motor vehicles based on box-Jenkins approach

Author Affiliations

  • 1Faculty of Engineering, Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
  • 2College of Distance Education, University of Cape Coast, Accra Regional Office, Papafio Hills, Ghana
  • 3Faculty of Engineering, Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana

Res. J. Mathematical & Statistical Sci., Volume 10, Issue (1), Pages 1-8, January,12 (2022)


The number of motor vehicles to be registered in a country is an important guiding standard for sustained economic growth. However, there are numerous challenges customers face during the registration exercise which has been given no scholarly attention. Here, Seasonal Autoregressive Integrated Moving Average (SARIMA) is proposed to provide future prediction of annual number of motor vehicles to be registered in Ghana has been developed. This study uses vehicles of all categories monthly registered dataset over five-years which was obtained from Driver and Vehicle Licensing Authority (DVLA) in Accra the capital city of Ghana to develop a SARIMA model by using Box-Jenkins approach for future prediction of motor vehicles to be registered at DVLA. In the modeling, the seasonality component in the dataset was taken care of by the process of differencing. The developed model performance was assessed based on good statistical indicators such as Mean Absolute Percentage Error (MAPE), Normalized Root Mean Square Error (NRMSE) and Relative Percentage Error (RPE). Results confirmed that the SARIMA model can be used to predict the number of motor vehicles to be registered annually in a country. This study is useful and a major contribution for modeling the expected number of motor vehicles of all categories to be registered in a country within the year.


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