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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Monthly pork price forecasting method based on Census X12-GM(1,1) combination model.

Chuansheng Wang1, Zhihua Sun2

  • 1Capital University of Economics and Business, Beijing, China.

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Accurate pork price forecasting is crucial. A new Census X12-GM(1,1) model improves prediction accuracy for China's fluctuating pork prices, outperforming traditional methods.

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Area of Science:

  • Agricultural Economics
  • Time Series Analysis
  • Econometrics

Background:

  • China's pork prices exhibit significant fluctuations, necessitating reliable forecasting.
  • Existing single prediction models lack sufficient accuracy for pork price trends.
  • Accurate pork price prediction is vital for stakeholders in the agricultural sector.

Purpose of the Study:

  • To develop and evaluate a novel combined forecasting model for monthly pork prices in China.
  • To enhance the accuracy of pork price predictions compared to existing single models.
  • To provide a more reliable reference for market participants and policymakers.

Main Methods:

  • Utilized monthly pork price data from January 2014 to December 2020.
  • Applied the Census X12 model to decompose pork price data into trend, cycle, and seasonal factors.
  • Employed the GM(1,1) model to forecast the trend and cycle components, integrating seasonal factors for final predictions.

Main Results:

  • The Census X12-GM(1,1) model demonstrated superior forecasting performance.
  • Achieved the lowest Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) compared to ARIMA, GM(1,1), and Holt-Winters models.
  • Provided specific forecasted pork prices for July-December 2020, indicating a downward trend.

Conclusions:

  • The Census X12-GM(1,1) combined model offers significantly improved prediction accuracy for monthly pork price series.
  • This enhanced accuracy makes the model a valuable tool for stakeholders.
  • The model provides a more reliable basis for decision-making in the pork market.