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

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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)

Abstract

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.

References

  1. Kumar, S.V. & Vanajakshi, L. (2015). Short-Term Flow Prediction Using Seasonal ARIMA Model with Limited Input Data. European Transport Research Review, 7, 1– 9., undefined, undefined
  2. Dargay, J., Gately, D. & Sommer, M. (2007). Vehicle Ownership and Income Growth. Worldwide: 1960-2030, 1-30., undefined, undefined
  3. Wahyudi, S.T. (2017). The ARIMA Model for Indonesia Stock Price. International Journal of Economics and Management, 11(1), 223-236., undefined, undefined
  4. Katimon, A., Shahid, S., & Mohsenipour, M. (2018). Modeling water quality and hydrological variables using ARIMA: a case study of Johor River, Malaysia. Sustainable Water Resources Management, 4(4), 991-998. https://doi.org/ 10.1007/s40899-017-0202-8., undefined, undefined
  5. Murat, M., Malinowska, I., Gos, M. & Krzyszczak, J. (2018). Forecasting Daily Meteorological Time Series Using ARIMA and Regression Models. Journal of International Agrophysics, 32, 253-264. https://doi.org/10.1515/intag-2017-0007., undefined, undefined
  6. Wang, S., Feng, J. & Liu, G. (2013). Application of Seasonal Time Series Model in the Precipitation Forecast. Mathematical and Computer Modelling, 58, 677-683., undefined, undefined
  7. Afrifa-Yamoah, E. & Saeed, B.I.I. (2016). Sarima Modeling and Forecasting of Monthly Rainfall in the Brong Ahafo Region of Ghana. World Environment, 6(1), 1-9. https://doi.org/10.5923/j.env.20160601.01., undefined, undefined
  8. Ruiz-Aguilar, J., Turias, J. & Jiménez-Come, M.J. (2014). Hybrid Approaches Based on SARIMA and Artificial Neural Networks for Inspection Time Series Forecasting. Transportation Research Part E, 67, 1-13. http://dx.doi.org/10.1016/j.tre.2014.03.009., undefined, undefined
  9. Qi, C., Zhang, D., Zhu, Y., Liu, L., Li, C., Wang, Z. & Li, X. (2020). SARFIMA Model Prediction for Infectious Diseases: Application to Hemorrhagic Fever with Renal Syndrome and Comparing with SARIMA. BMC Medical Research Methodology, 20(243), 1-7. https://doi.org/10.1186/s12874-020-01130-8., undefined, undefined
  10. Suksawang, P., Suphachan, S. & Kaewnuch, K. (2018). Electricity Consumption Forecasting in Thailand Using Hybrid Model SARIMA and Gaussian Process with Combine Kernel Function Technique. International Journal of Energy Economics and Policy, 8(4), 98-109., undefined, undefined
  11. Phamotse, I.M. & Lues, L. (2010). Assessing Practices During the Vehicle Registration Process in Lesotho. Journal for New Generation Sciences, 8(1), 190-203., undefined, undefined
  12. Le Vine, S., Wu, C., & Polak, J. (2018). A nationwide study of factors associated with household car ownership in China. IATSS research, 42(3), 128-137., undefined, undefined
  13. Boah-Mensah, E. (2013). The Number of Cars in Ghana Increase by 23%. www.vehicle population in Ghana. (Accessed March 19, 2015)., undefined, undefined
  14. Agunbiade, D.A. & Peter, E.N. (2013). Modeling and Forecasting Vehicle Registration System: An ARMA Approach. Society for Mathematical Services and Standards, 2(1), 1-13., undefined, undefined
  15. Abu, N. & Ismail, Z. (2019). A Study on Private Vehicle Demand Forecasting Based on Box-Jenkins method. AIP Conference Proceedings 2059, 1-9. AIP Publishing LLC. https://doi.org/10.1063/1.5085948., undefined, undefined
  16. Anvari, S., Tuna, S., Canci, M. & Turkay, M. (2016). Automated Box-Jenkins Forecasting tool with an Application for Passenger Demand in Urban Rail Systems. Journal of Advanced Transportation, 50, 25-49. https://doi.org/10.1002/atr.1332 ., undefined, undefined
  17. Kirchgässner, G., Wolters, J. & Hassler, U. (2013). Univariate Stationary Processes. Introduction to Modern Time Series Analysis, Springer, 27-93., undefined, undefined
  18. Walter, E. & Pascalau, R. (2015). Pretesting for multi-step-ahead exchange rate forecasts with STAR models. International Journal of Forecasting, 31(2), 473-487., undefined, undefined
  19. Hyndman, J.R. & Athanasopoulos, G. (2018). Forecasting: Principles and Practice., undefined, undefined
  20. Aman, S., Simmhan, Y., & Prasanna, V. K. (2014). Holistic measures for evaluating prediction models in smart grids. IEEE Transactions on Knowledge and Data Engineering, 27(2), 475-488., undefined, undefined
  21. Wang, C. N., & Phan, V. T. (2014). An improvement the accuracy of grey forecasting model for cargo throughput in international commercial ports of Kaohsiung. International Journal of Business and Economics Research, 3(1), 1-5., undefined, undefined
  22. Chen, C. & Hsin, P. (2016). Forecasting of Taiwan’s Gross Domestic Product Using the Novel Nonlinear Grey Bernoulli Model with ANN Error Correction. Journal of Global Economics, 4, 1-4., undefined, undefined
  23. Moonchai, S. & Rakpuang, W. (2015). A New Approach to Improve Accuracy of Grey Model GMC (1, n) in Time Series P. Modelling and Simulation in Engineering, 1-10. http://dx.doi.org/10.1155/2015/126738, undefined, undefined