FORECASTING OF RAMBUTAN PRICES VIA THE USE OF STATISTICAL METHODS
Keywords:
Rambutan, Box-Jenkins, Exponential Smoothing, Decomposition, Combining ForecastsAbstract
The objective of this study was to forecast the rambutan prices via the use of statistical methods. The monthly average data, which were gathered from the website of the Office of Agricultural Economics during January 2005 to May 2020 (185 months) were divided into 2 datasets. The first dataset, which consisted of 180 months from January 2005 to December 2019 was used for constructing the forecasting models via the use of 10 statistical methods. The second dataset, which consisted of 5 months from January to May 2020 was used for comparing the accuracy of the forecasting model via the lowest root mean square error. The results indicated that the most accurate method was the combining forecasts method with a model: t = 2.83552 + 1.11825SimpleS - 0.26902WinterAdd. This forecast model had a forecast error of only 3.34 Baht/kg. Therefore it can be used as a guideline for farmers and rambutan production agencies to plan the planting accordingly.
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References
Morton, J. F. (1987). Fruits of Warm Climates. Winterville, N.C.: Creative Resource Systems, Inc.
The Rambutan Information Website. (2020). Rambutan: Exotic Fruit of Southeast Asia. Retrieved from https://rambutan.com/
Ungvichian, I. (2003). Rambutan. Thailand Institute of Scientific and Technological Research, Agricultural Technology Department, Ministry of Agriculture and Cooperatives.
Exim Knowledge Center. (2019). The Office of Agricultural Economics has predicted the output of Rambutan in the Eastern region year 2020. Retrieved from https://kmc.exim.go.th/detail/economy-news/20191223140440
Exim Knowledge Center. (2020). The Office of Agricultural Economics has predicted the output of Rambutan in the South region year 2020. Retrieved from https://kmc.exim.go.th/detail/economy-news/20200331123414
The Office of Agricultural Economics. (2020). Table 3 prices of agricultural products that farmers can sell at farmland. Retrieved from http://www.oae.go.th/view/1/ดัชนีราคาและผลผลิต/TH-TH
Riansut, W. (2018). A comparison of Forecasting Models of Longan Price. Journal of Agricultural Research and Extension, 35(3), 73-83.
Riansut, W. (2018). Comparison of Tangerine Prices Forecast Model by Exponential Smoothing Methods. Thai Journal of Science and Technology, 7(Supplement)(5), 460-470.
Riansut, W. (2020). Selection of the Appropriate Banana Gold Prices Forecasting Models. Thai Science and Technology Journal, 28(1), 14-25.
Riansut, W. (2020). Selection of Forecasting Models for the Mango Prices. Srinakharinwirot Research and Development (Journal of Humanities and Social Sciences), 12(23), 52-62.
Ket-iam, S. (2005). Forecasting Technique (2nd ed.). Songkhla: Thaksin University.
Manmin, M. (2006). Time Series and Forecasting. Bangkok: Foreprinting.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control. 3rd ed. New Jersey: Prentice Hall.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2006). Introduction to Linear Regression Analysis (4th ed.) New York: John Wiley Sons.
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