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Forecasting the Agriculture Output Values in China Based on Grey Seasonal Model

Author

Listed:
  • Yan Chen
  • Li Nu
  • Lifeng Wu

Abstract

The output values for agriculture, forestry, animal husbandry, and fishery are important indicators of agricultural economic development. Therefore, accurately predicting the output values for agriculture, forestry, animal husbandry, and fishery can capture the developmental trend and the optimize the structure. Agriculture, forestry, animal husbandry, and fishery are typical seasonal industries, and thus their output values vary greatly among different seasons. To accurately estimate the seasonal variations in the observed sequence and obtain better prediction results, the output values for agriculture, forestry, animal husbandry, and fishery in different quarters from 2018 to 2021 are predicted and analyzed by using the grey seasonal model (GSM). The results indicated that the prediction accuracy of GSM is relatively high. The output values for the agriculture, forestry, animal husbandry, and fishery as well as their total output value will increase gradually. It is an important achievement of structural reform under the new normal economic situation. In addition, the GSM provides a new method for predicting seasonal data.

Suggested Citation

  • Yan Chen & Li Nu & Lifeng Wu, 2020. "Forecasting the Agriculture Output Values in China Based on Grey Seasonal Model," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, August.
    • Handle: RePEc:hin:jnlmpe:3151048
    DOI: 10.1155/2020/3151048
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    Cited by:

    1. He Li & Hua He & Jian Zhang, 2022. "Study on Rural Development Evaluation and Drivers of Sustainable Development: Evidence from the Beijing-Tianjin-Hebei Region of China," Sustainability, MDPI, vol. 14(15), pages 1-21, August.
    2. Sebastian C. Ibañez & Christopher P. Monterola, 2023. "A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers," Agriculture, MDPI, vol. 13(9), pages 1-27, September.

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