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The Application of Combination Forecasting Model in Forecasting the Total Power of Agricultural Machinery in Heilongjiang Province

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  • HAO, Xiaoling
  • SUO, Ruixia

Abstract

Agricultural machinery total power is an important index to reflect and evaluate the level of agricultural mechanization. Firstly, we respectively made use of exponential model, grey forecasting and BP neural network to construct models depending on historical data of agricultural machinery total power of Heilongjiang Province; secondly, we constructed the combined forecasting models that respectively based on divergence coefficient method, quadratic programming and weight distribution of Shapley value. Fitting results showed that the various combination forecasting model is superior to the single models. Finally, we applied the combination forecasting model which based on the weight distribution method of Shapley value to forecast Heilongjiang agricultural machinery total power, and it would provide some reference to the development and program for power of agriculture machinery．

Suggested Citation

  • HAO, Xiaoling & SUO, Ruixia, 2015. "The Application of Combination Forecasting Model in Forecasting the Total Power of Agricultural Machinery in Heilongjiang Province," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 7(05), pages 1-4, May.
    • Handle: RePEc:ags:asagre:207035
    DOI: 10.22004/ag.econ.207035
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    Cited by:

    1. Zhenni Ding & Huayou Chen & Ligang Zhou, 2023. "Using shapely values to define subgroups of forecasts for combining," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 905-923, July.

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    Keywords

    Agribusiness;

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