Research on the Optimal Combination Forecasting Model for Vegetable Price in Hainan

  • Lu Ye
  • Yuping Li
  • Yanqun Liu
  • Xiaoli Qin
  • Weihong Liang
Conference paper

Abstract

Hainan is the national people’s “Vegetable Basket” in winter. It is of great significance to accurately predict vegetable market price in Hainan for farmers cultivating “vegetable garden” good and government holding “vegetable basket” steady. The theory of combination forecasting is practicable in complex economic system. In view of complexity of vegetable market system, by using the data of vegetable market price in Hainan, three models are set up separately which are Triple exponential smoothing model, simple linear regression model, and grey forecasting model. Then, an optimal combination forecasting model is constructed based on three models above. The prediction results show that the prediction accuracy of the optimal combination forecasting model is superior to the single model, and the model overcomes limitation of the single model and effectively improves the prediction results of vegetable market price.

Keywords

Vegetable price Triple exponential smoothing model Simple linear regression model Grey forecasting model Optimal combination forecasting model 

References

  1. Ding Yongmei (2004) The application and some discussion of combination forecast about Chinese stock market. Huazhong University of Science and Technology: Wuhan, Hubei, pp 9–10Google Scholar
  2. Li Ganqiong, Xu Shiwei, Li Zhemin et al. (2010) Study on super short-term forecasting for market price of agro-products—based on modern times series modeling of daily wholesale price of tomatoes. J Huazhong Agric Univ (Soc Sci Edn) (6):40–41Google Scholar
  3. Li Shanshan (2012) Combination forecasting based on regression and index smoothness. J Taiyuan Norm Univ (Nat Sci Edn) 11(1):75–77Google Scholar
  4. Liu Dongjun, Zhou Zhihong (2011) Applications of gray forecast model combined with artificial neural networks model to water quality forecast. Syst Eng 29(9):105–109Google Scholar
  5. Liu Sifeng, Xie Naiming (2008) Grey system theory and application, 4th edn. Science press, Beijing, pp 96–99Google Scholar
  6. Liu Xiaoxu (2009) Comparing for grey forecast and forecast of one element linear regression. J Sichuan Univ Sci Eng (Nat Sci Edn) 22(1):107–109Google Scholar
  7. Luo Changshou (2011) Research on the prediction method of vegetable price based on neural. Bull Sci Technol 27(6):881–885Google Scholar
  8. Price Bureau of Hainan Province (2013) Price monitoring, http://www.hnpi.net/. 13 Mar 2013
  9. Shen Chen, Mu Yueying (2011) Time series change analysis of vegetable prices in China. Stat Decis (16):78–80Google Scholar
  10. Sun Nan (2004) The application of optimum forecast combining in Tianjin health manpower resource needs. Tianjin Medical University: Tianjian, pp 20–22Google Scholar
  11. Xu Chao (2010) Multiple regression analysis on variable factor of China soybean future price and judgment on subsequent price trend. China Price (6):30–33Google Scholar
  12. Zhang Jinshan, Xie Xiangtian (2011) Vegetable price prediction based on artificial neural network. Jiangsu Commer Forum (4):47–60Google Scholar
  13. Zhu Xiaoxia (2012) Analysis and prediction of vegetable price fluctuation cycles based on Markov chain. Product Res (8):143–146Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Lu Ye
    • 1
  • Yuping Li
    • 1
  • Yanqun Liu
    • 1
  • Xiaoli Qin
    • 1
  • Weihong Liang
    • 1
  1. 1.Institute of Scientific and Technical Information, CATASKey Lab of Tropical Crops Information Technology Application Research of Hainan ProvinceHainan DanzhouChina

Personalised recommendations