Fourier Regression and ARIMAX Model for Forecasting Monthong Durian Price Index

Main Article Content

Kanittha Yimnak*
Rungsarit Intaramo

Abstract

The purposes of this study were to create models for forecasting the Monthong durian price index at the farm and to compare the forecasting efficiency of ARIMAX model (Autoregressive Integrated Moving Average with Exogenous Variable model) and Fourier Regression model. The data set was collected monthly between January 2014 and December 2020 (a total of 84 months). Production index and China consumer confidence were used as explanatory variables. The efficiency of both methods was compared by Mean Absolute Percentage Error (MAPE). From the result, we found that the ARIMAX (1,1,1) and Fourier regression models were both suitable for forecasting the Monthong durian price index. However, the MAPE value obtained from the ARIMAX model was 3.365 times higher than that obtained from Fourier regression model, suggesting that the Fourier regression model is more efficient. 


Keywords: Monthong durian price index; China consumer confidence; Fourier regression; ARIMAX model


*Correspondence author: Tel.: +66 86-4182542


                                                E-mail: kanittha_y@rmutt.ac.th

Article Details

Section
Original Research Articles

References

Isvilanonda, S., 2019. The Situation of the World Production and Consumption of Durian and Thai Exports of Durian, Document Assembled in the Forum "Look at the future of Thai Export Durian Market". [online] Available at: https://www.slideshare.net/somporn isvilanonda/durian-production-and-consumption-and-thailand-export-70619

Office of Agricultural Economics, 2020. Agricultural Production Index. [online] Available at: http://www.oae.go.th/assets/portals/1/fileups/aeocdata/files/Table1_th_agriPriceIndex_02_64.XLS

Office of Agricultural Economics, 2020. Agricultural Price Index. [online] Available at: http://www.oae.go.th/assets/portals/1/fileups/aeocdata/files/Table2_th_agriPriceIndex_02_64.XLS

National Bureau of Statistics of China, 2020. China Consumer Confidence. [online] Available at: https://tradingeconomics.com/china/consumer-confidence

Dhakonlayodhin, B. and Areepong, Y., 2018. A comparison of forecasting models of stock price using ARIMA and ARIMAX models. Huachiew Chalermprakiet Science and Technology Journal, 4(1), 44-55.

Anggraeni, W., Vinarti, R. and Kurniawati, Y., 2015. Performance comparisons between Arima and Arimax method in moslem kids clothes demand forecasting: Case study. Procedia Computer Science, 72, 630-637.

Prahutama, A., Suparti and Utami, T.W., 2018. Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia. Journal of Physics: Conference Series, 974, 012067, https://doi.org/10.1088/1742-6596/974/1/012067

Bilodeau, M., 1992. Fourier Smoother and Additive Models. The Canadian Journal of Statistics, 3, 257-259.

Semiati, R., 2010. Fourier Birespon Series Nonparametric Regression. Thesis. ITS, Surabaya.

Asrini, L.J., 2012. Fourier series parametric regression. Proceedings of the National Seminar FMIPA Universitas Negeri Surabaya, 24 November 2012, Surabaya, Indonesia, pp. 77-80.

Tiao, G.C. and Box, G.E.P., 1981. Modelling multiple time series with applications. Journal of the American Statistical Association, 76(376), 802-816.

Yang, M., Xie, J., Mao, P., Wang, C. and Ye, Z., 2017. Application of the ARIMAX model on forecasting freeway traffic flow. 17th COTA International Conference of Transportation Professionals, 7-9 July 2017, Shanghai, China.

Drapper, N.R and Smith, H., 1992. Applied Regression Analysis. 2nd ed. New York: Marcel Dekker.

Eubank, R.L.,1988. Spline Smoothing and Nonparametric Regression. New York: Marcel Dekker.