Grey Holt-Winters Model and Grey Wolf Optimization for the Egg Price Forecasting in China
DOI:
https://doi.org/10.52812/ijgs.112Keywords:
Grey Holt-Winters Model, Grey Wolf Optimizer, Support Vector Regression, Egg Price, ForecastingAbstract
To deal with the egg price forecasting in China, the grey Holt-Winters model is optimized by the Grey Wolf Algorithm. The optimized grey Holt-Winters model outperform support vector regression for forecasting the price of eggs in the four provinces of China. The seasonality of egg price is also discussed in the current study. The forecast accuracy is gauged through the Mean Absolute Percent Error and the Root Mean Square Error. The proposed model's forecasts will provide the egg producers and consumers with better long-term information.
References
Chen, S. X., Lei, L., & Tu, Y. (2016). Functioal coefficient moving average model with applications to forecasting Chinese CPI. Statistica Sinica, 26(4), 1649-1672. http://www.jstor.org/stable/ 44114352
Chen, X. (2024). Analysis and prediction of egg price in Guangdong Province based on ARIMA model and Auto-regressive model. Statistics and Application, 13(2), 84798. https://doi.org/10.12677/sa.2024.132036
Conrad, Z., C., Johnson, L. K., Roemmich, J. N., Juan, W., & Jahns, L. (2017). Time trends and patterns of reported egg consumption in the U.S. by sociodemographic characteristics. Nutrients, 9(4), 333. https://doi.org/10.3390/nu9040333
Dantas, T. M., Oliveira, F. L. C., & Repolho, H. M. V. (2017). Air transportation demand forecast through Bagging Holt Winters methods. Journal of Air Transport Management, 59, 116-123. https://doi.org/10.1016/j.jairtraman.2016.12.006
Dobrowolska, A., & Brown, S. (2016). The economic impact of the 2015 avian influenza outbreak on U.S. egg prices. Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management (St. Louis, MO). https://doi.org/10.22004/ag.econ.285859
Faris, H., Aljarah, I., Al-Betar, M.A., & Mirjalili, S. (2018). Grey wolf optimizer: a review of recent variants and applications. Neural Computing and Applications, 30, 413–435. https://doi.org/10.1007/s00521-017-3272-5
Hebbar, A.N., Patted, N. P., & Mitrannavar, D.H. (2016). Dynamics of egg prices in major markets of India: an econometric analysis. International Journal of Agricultural and Statistical Sciences, 12, 151-157. https://connectjournals.com/file_full_text/2578801H_151-157.pdf
Li, Z. M., Cui, L. G., Xu, S. W., et al. (2013). Prediction model of weekly retail price for eggs based on chaotic neural network. Journal of Integrative Agriculture, 12(12), 2292-2299. https://doi.org/10.1016/S2095-3119(13)60610-3
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Nilsen, Ø. A., Sørgard, L., & Ulsaker, S. A. (2016). Upstream merger in a successive oligopoly: Who pays the price?. International Journal of Industrial Organization, 48, 143-172. https://doi.org/10.1016/j.ijindorg.2016.06.003
Oguri, K., Adachi, H., Yi, C. H., Cho, Y., & Sugiyama, M. (1992). Study on egg price forecasting in Japan. Research Bulletin of the Faculty College of Agriculture Gifu University, 57, 157-164. https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=200902055419454355
Petropoulos, F., Wang, X., & Disney, S. M. (2019). The inventory performance of forecasting methods: Evidence from the M3 competition data. International Journal of Forecasting, 35(1), 251-265. https://doi.org/10.1016/j.ijforecast.2018.01.004
Seok, J. H., Kim, G., Reed, M. R., & Kim, S.-E. (2018). The impact of avian influenza on the Korean egg market: Who benefited?. Journal of Policy Modeling, 40(1), 151-165. https://doi.org/10.1016/j.jpolmod.2017.11.003
Wang, D., & He, Y. (2015). Forecasting of the egg price based on EEMD. Asian Agricultural Research, 7(7), 1-4. https://doi.org/10.22004/ag.econ.209836
Xu, S. W., Dong, X. X., Li, Z. M., & Li, G. Q. (2011). Vertical price transmission in the Chinas layer industry chain: an application of FDL approach. Agricultural Sciences in China, 10(11),1812- 1823. https://doi.org/10.1016/S1671-2927(11)60181-8
Yang, Z., Rose, S. P., Yang, H. M., Pirgozliev, V., & Wang, Z. Y. (2018). Egg production in China. World's Poultry Science Journal, 74, 1-10. https://doi.org/10.1017/S0043933918000429
Zhang, K. R., & Liu, W. Y. (2015). Empirical prediction and risk assessment of chicken egg prices in China using support vector machine algorithm. American Journal of Food Technology, 10(5), 223-240. https://doi.org/10.3923/ajft.2015.223.240
Zhao, H., & Wu, L. (2020). Forecasting the non-renewable energy consumption by an adjacent accumulation grey model. Journal of Cleaner Production, 275, 124113. https://doi.org/10.1016/j.jclepro.2020.124113
.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Science Insight
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons Non Commercial CC BY-NC: The work is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is properly attributed.
