Forecasting Customs Revenue Collection in light of the spread of the COVID-19 pandemic using ARIMA models and the exponential smoothing methods in Libya.

Authors

  • Mohamed AH Milad Data analysis and computer, Faculty of Economics & Commerce, University of Elmergib, Libya

Keywords:

ARIMA, COVID-19 Pandemic, Customs revenue collection, Exponential smoothing, Forecast

Abstract

Forecasting future values of economic variables is one of the most critical tasks for governments, especially the values related to customs revenue collection are to be forecasted efficiently as the need for planning is great in this sector, because it is considered one of the sources of funding for the state's public treasury. The main objective of this research is to identify an appropriate statistical model for time series forecasting customs revenue collection during the current COVID-19 pandemic in Libya. The decision throughout this research is mainly concerned with ARIMA model, and Simple, Brown’s linear trend, exponential smoothing methods. The obtained data covers 108 observations, starting from the first week of the 6th month of the year 2019 to the last week of the 8th month of the year 2021.Based on the forecasting results of the current research, it was revealed that ARIMA (0,1,1) model offered more probabilistic information that improves forecasting the volume of customs revenue collection in light of the COVID-19 pandemic. According to this model, the research forecasts the new period in the next eight weeks or two months and finds that it will be increasing. In this research, ARIMA model and exponential smoothing methods are linear models based on the reactions to customs revenue collection due to the spread of the COVID-19 pandemic in the world. Furthermore, the forecasting performance between linear and nonlinear models can be compared in future studies

References

Alzahrani, S.I., I.A. Aljamaan, and E.A. Al-Fakih, Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions. Journal of infection and public health, 2020. 13(7): p. 914-919.

Milad, M.A.H., Forecasting the Volume of Customs Revenue Collection in Light of the COVID-19 Pandemic in Libya. The Covid-19 pandemic conference: the reality and the economic and political perspectives in the Mediterranean countries, 2020: p. 96-108.

Liu, Comparison of regression and ARIMA models with neural network models to forecast the daily streamflow of White Clay Creek. 2011: University of Delaware.

Brown-Smoothing, R., Forecasting and Prediction of Discrete Time Series. Engelwood Cliffs, New York, 1963.

Winters, P.R., Forecasting sales by exponentially weighted moving averages. Management science, 1960. 6(3): p. 324-342.

Yu, C., et al., Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model. Infection and Drug Resistance, 2021. 14: p. 2809-2821.

Laiqing, H., X. Linping, and M. Hong, The application of exponential smoothing methods on the forecast of downy mildew disease trends. Journal of Mathematical Medicine, 2006. 2.

Li, J., et al. The comparison of arma, exponential smoothing and seasonal index model for predicting incidence of newcastle disease. in 2010 World Automation Congress. 2010. IEEE.

Liu, H., et al., Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models. Journal of infection and public health, 2020. 13(2): p. 287-294.

Munarsih, E. and I. Saluza. Comparison of exponential smoothing method and autoregressive integrated moving average (ARIMA) method in predicting dengue fever cases in the city of Palembang. in Journal of Physics: Conference Series. 2020. IOP Publishing.

Mahmood, N.S., Forecasting on tuberculosis [TB]: a comparison between ARIMA and holt winter’s exponential smoothing models. 2021, Universiti Teknologi Mara Perlis.

Yiyi, Z., Z. Qi, and F. Wei, The Application of Exponential Smoothing Methods on the Forecast of Hepatitis A in Shanghai. Chinese Journal of Health Statistics, 2013: p. 01.

Xue, J.L., et al., Forecast of the number of patients with end-stage renal disease in the United States to the year 2010. Journal of the American Society of Nephrology, 2001. 12(12): p. 2753-2758.

Wieczorek, M., et al., Real-time neural network based predictor for cov19 virus spread. Plos one, 2020. 15(12): p. e0243189.

Wieczorek, M., J. Siłka, and M. Woźniak, Neural network powered COVID-19 spread forecasting model. Chaos, Solitons & Fractals, 2020. 140: p. 110203.

Alabdulrazzaq, H., et al., On the accuracy of ARIMA based prediction of COVID-19 spread. Results in Physics, 2021: p. 104509.

Benvenuto, D., et al., Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief, 2020. 29: p. 105340.

Bezerra, A.K.L. and É.M.C. Santos, Prediction the daily number of confirmed cases of COVID-19 in Sudan with ARIMA and Holt Winter exponential smoothing. International Journal of Development Research, 2020. 10(08): p. 39408-39413.

Yonar, H., et al., Modeling and Forecasting for the number of cases of the COVID-19 pandemic with the Curve Estimation Models, the Box-Jenkins and Exponential Smoothing Methods. EJMO, 2020. 4(2): p. 160-165.

Satrio, C.B.A., et al., Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science, 2021. 179: p. 524-532.

Yue, X.-G., et al., Risk prediction and assessment: duration, infections, and death toll of the COVID-19 and its impact on China’s economy. Journal of Risk and Financial Management, 2020. 13(4): p. 66.

Papastefanopoulos, V., P. Linardatos, and S. Kotsiantis, COVID-19: a comparison of time series methods to forecast percentage of active cases per population. Applied sciences, 2020. 10(11): p. 3880.

Wabomba, M.S., M.m.P. Mutwiri, and M. Fredrick, Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. Science Journal of Applied Mathematics and Statistics, 2016. 4(2): p. 64-73.

Ball, R.D., Bayesian methods for quantitative trait loci mapping based on model selection: approximate analysis using the Bayesian information criterion. Genetics, 2001. 159(3): p. 1351-1364.

Milad, M. and I.B.I. Ross, A Robust Composite Model Approach for Forecasting Malaysian Imports: A Comparative Study. Journal of Applied Sciences, 2016. 16(6): p. 279-285.

Milad, M., I.B.I. Ross, and S. Marappan, Regression analysis to forecast Malaysia's imports of crude material. International Journal of Management and Applied Sciences, 2015. 1: p. 121-130.

Fisher, T.J., Testing Adequacy of ARMA Models using a Weighted Portmanteau Test on the Residual Autocorrelations. Contributed Paper, 2011. 327: p. 2011.

Chatfield, C., Time-series forecasting. 2000: Chapman and Hall/CRC.

Brockwell, P.J. and R.A. Davis, Time series: theory and methods. 2013: Springer Science & Business Media.

Enders, W., Applied econometric time series. 2008: John Wiley & Sons.

Granger, C.W., Some properties of time series data and their use in econometric model specification. Journal of econometrics, 1981. 16(1): p. 121-130.

Salman, A.G. and B. Kanigoro, Visibility Forecasting Using Autoregressive Integrated Moving Average (ARIMA) Models. Procedia Computer Science, 2021. 179: p. 252-259.

Hamilton, J.D., Time series analysis. Vol. 2. 1994: Princeton university press Princeton.

Khin, A.A., et al. Stationarity test with a direct test for heteroskedasticity in exchange rate forecasting models. in AIP Conference Proceedings. 2017. American Institute of Physics.

Lin, S.-N., et al., Economic design of autoregressive moving average control chart using genetic algorithms. Expert systems with applications, 2012. 39(2): p. 1793-1798.

Ostertagová, E. and O. Ostertag, Forecasting using simple exponential smoothing method. 2012.

Soubhik Chakraborty, S.S., Swarima Tewari and Mita Pal, an intersting application of simple exponential smoothing in music analysis. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 2013. 2: p. 37-44.

Ramasubramanian, V., Time-series analysis, modelling and forecasting using SAS software.

Fomby, T.B., Exponential smoothing models. 2008, Retrieved from Southern Methodist University website: http://www. google. com/url.

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Published

2021-12-31

How to Cite

milad, mohamed A. (2021). Forecasting Customs Revenue Collection in light of the spread of the COVID-19 pandemic using ARIMA models and the exponential smoothing methods in Libya . Journal of Alasmarya University, 6(4), 167–183. Retrieved from http://journal.com.ly/index.php/Jau/article/view/513