Double exponential smoothing and Holt-Winters methods with optimal initial values and weighting factors for forecasting lime, Thai chili and lemongrass prices in Thailand

Article Sidebar

PDF
Published: Feb 28, 2018
Keywords:
Forecasting Double exponential smoothing Holt-Winters Lime Thai chili Lemongrass

Main Article Content

Thitima Booranawong
Faculty of Management Sciences, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat 80280, Thailand
Apidet Booranawong
Faculty of Engineering, Thaksin University, Phatthalung Campus, Phatthalung 93210, Thailand

Abstract

In this paper, the Double Exponential Smoothing (DES), the Multiplicative Holt-Winters (MHW) and the Additive Holt-Winters (AHW) methods with optimal initial values and weighting factors are presented to forecast lime, Thai chili, and lemongrass prices in Thailand. Since these plants are important economic plants in Thailand, knowing their market prices or trends before selling them would be very useful for Thai agriculturists to appropriately plan their work and sales. In this paper, lime, Thai chili and lemongrass prices from January 2011 to September 2016 were gathered from the website’s database of Simummuang market used and as input data.  This is one of the biggest markets in Thailand. Our study reveals that the DES method with optimal initial values and weighting factors provides the smallest forecasting error measured by the Mean Absolute Percentage Error (MAPE) when forecasting Thai chili and lemongrass prices, while the MHW and the AHW methods show better performance on forecasting lime price data which present seasonality patterns. The forecasted prices of lime, Thai chili and lemongrass during October 2016 to December 2016 are also provided. 

Article Details

How to Cite
Booranawong, T., & Booranawong, A. (2018). Double exponential smoothing and Holt-Winters methods with optimal initial values and weighting factors for forecasting lime, Thai chili and lemongrass prices in Thailand. Engineering and Applied Science Research, 45(1), 32–38. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/73088
Issue
Vol. 45 No. 1 (2018)
Section
ORIGINAL RESEARCH

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Brown RG. Exponential smoothing for predicting demand. The 10th National Meeting of the Operations Research Society of America, San Francisco; 1956.

Hunter JH. The exponentially weighted moving average. Journal of Quality Technology 1986;18:203-210.

Winters P. Forecasting sales by exponentially weighted moving averages. Management Science 1960;6:324–342.

Montgomery DC, Jennings CL, Kulahci M. 2008. Introduction to time series analysis and forecasting. Wiley Series in Probability and Statistics. John Wiley and Sons, Hoboken, New Jersey, USA; 2008.

Kalekar PS. Time series forecasting using Holt-Winters exponential smoothing. Kanwal Rekhi School of Information Technology; 2004.

Holt CC. Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting 2004;20:5–10.

Dhakre DS, Sarkar KA, Manna S. Forecasting price of Brinjal by Holt Winters method in west Bengal using MS excel. International Journal of Bio-resource, Environment and Agricultural Sciences 2016;2: 232-235.

Gardner ES. Exponential smoothing: the state of the art. Journal of Forecasting 1985;4:1–28.

De Gooijer JG, Hyndman RJ. 25 years of time series forecasting. International Journal of Forecast 2006;22:443–473.

Tratar LF, Srmcnik E. The comparison of Holt-Winters method and regression methods: a case study. Energy 2016;109:266-276.

Chatfield C. Time-series forecasting. Chapman & Hall, New York; 2001.