Forecasting Malaysia Bulk Latex Prices Using Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing
Keywords:natural rubber, time series, forecasting, ARIMA, Exponential Smoothing.
Natural rubber is an important component of many developed countries' socioeconomic structures because it is frequently used to manufacture essential consumer goods such as tires and latex gloves. The natural rubber industry is heavily affected by the volatility and unpredictability of the natural bulk latex markets. Forecasting natural rubber prices is critical for rubber industry in procurement decisions and marketing strategies. This study aims to model monthly bulk latex prices in Malaysia using Autoregressive Integrated Moving Averages (ARIMA) and Exponential Smoothing. The models performance are measured using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The Malaysian Rubber Board has 132 historical prices for the latex in Malaysia from January 2010 to December 2020. They are used for training and testing in determining the forecasting accuracy. Overall finding show that ARIMA (1,1,0) provides the most accurate prediction. With a MAPE of 8.59 percent and an RMSE of 69.78 sen per kilogram, this model is considered the best and highly accurate.
Goh, H. H., Tan, K. L., Khor, C. Y., & Ng, S. L. "Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis," Journal of Quantitative Economics, 14(2), pp. 323–344, 2016.
Ismai, Z., Abu, N., & Sufahani, S. "New product forecasting with limited or no data,". AIP Conference Proceedings, 1782, 2016.
Zahari, F. Z., Khalid, K., Roslan, R., Sufahani, S., Mohamad, M., Rusiman, M. S., & Ali, M. "Forecasting Natural Rubber Price in Malaysia Using Arima," Journal of Physics: Conference Series, 995(1), pp. 0–7, 2018.
Vijayakumar, A.N. "International Determinants on Indian Rubber Prices," SJCC Management Research Review. Vol. 9, 2019.
Chawananon, C. "Factors affecting the Thai Natural rubber market Equilibrium: demand and supply response analysis using two stage least squares approach'" 2014.
Ramli, N., Md Noor, A. H. S., Sarmidi, T., Said, F. F., & Azam, A. H. M. "Modelling the volatility of rubber prices in ASEAN-3," International Journal of Business and Society, 20(1), pp.1–18. 2019.
Rajput, H., Changotra, R., Rajput, P., Gautam, S., Gollakota, A. R. K., & Arora, A. S. "A shock like no other: coronavirus rattles commodity markets," Environment, Development and Sustainability. 2020.
Cherdchoongam, S., & Rungreunganun, V. "Forecasting the Price of Natural Rubber in Thailand Using the ARIMA Model," King Mongkut's University of Technology North Bangkok International Journal of Applied Science and Technology, 9(4), pp. 271–277, 2016.
Udomraksasakul, C., & Rungreunganun, V. "Forecasting the Price of Field Latex in the Area of Southeast Coast of Thailand Using the ARIMA Model," 13(1), 550–556, 2018.
Khin, A. A., Thambiah, S., & Teng, K. L. L. "Short-term and long-term price forecasting models for the future exchange of Malaysian natural rubber market," International Journal of Agricultural Resources, Governance and Ecology, 13(1), 21–42, 2017.
Winters, P. R. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, 6(3), pp. 324–342, 1960.
Hammad, M. A., Jereb, B., Rosi, B., & Dragan, D. "Methods and Models for Electric Load Forecasting: A Comprehensive Review," Logistics & Sustainable Transport, 11(1), 51–76, 2020.
Petropoulos, F., Hyndman, R. J., & Bergmeir, C. "Exploring the sources of uncertainty: Why does bagging for time series forecasting work?," European Journal of Operational Research, 268(2), pp. 545–554, 2018.
Bandyopadhyay, G., & Guha, B. "Gold Price Forecasting Using ARIMA Model," Journal of Advanced Management Science, 4(2), pp. 117–121, 2016.
Anderson, T. W., & Darling, D. A. "A Test of Goodness of Fit," Journal of the American Statistical Association, 49(268), pp. 765–769, 1954.
Karia, A. A., & Bujang, I. "Progress accuracy of CPO price prediction: Evidence from ARMA family and artificial neural network approach," International Research Journal of Finance and Economics, 64(64), pp. 66–79, 2011.
Lewis, C. D. "Industrial and business forecasting methods: a practical guide to exponential smoothing and curve fitting," Butterworth Scientific, 1982.
"Natural Rubber Market Review". Lembaga Getah Malaysia, November. http://www3.lgm.gov.my/digest/ digest/digest-11-2020.pdf, 2020.
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