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Related Experiment Videos

Comparison of forecasting methodologies using egg price as a test case.

H A Ahmad1, M Mariano

  • 1Tuskegee University, BIMS, 112 Williams-Bowie Hall, Tuskegee, AL 36088, USA. ahmadh@tuskegee.edu

Poultry Science
|April 18, 2006
PubMed
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Neural networks offer a more reliable method for forecasting egg prices compared to traditional regression analysis. This approach better captures price variations by recognizing patterns in historical data.

Area of Science:

  • Agricultural Economics
  • Computational Intelligence
  • Time Series Forecasting

Background:

  • Egg price forecasting is challenging, with traditional regression models often failing to explain price variations effectively.
  • Existing methods rely on regression analysis and expert intuition, which may not yield high confidence predictions.

Purpose of the Study:

  • To investigate the efficacy of artificial neural networks for forecasting shelled egg prices.
  • To compare the predictive performance of neural networks against traditional regression analysis.

Main Methods:

  • Utilized Urner Barry egg quotes (1991-2002) and USDA data (1993-2000) on hen numbers, storage capacity, and hatching eggs.
  • Implemented and compared three neural network models: Ward, back-propagation, and general regression neural networks.

Related Experiment Videos

  • Assessed model performance against standard regression analysis.

    • Main Results:

    • Regression analysis explained only 37% of egg price variation.
    • Neural networks demonstrated a tighter fit to historical egg price data.
    • General regression neural networks achieved an R-squared value as high as 60%.

    Conclusions:

    • Neural networks show greater reliability in egg price forecasting than simple regression analysis.
    • Effective data collection and manipulation are crucial for successful neural network implementation in price prediction.
    • Artificial neural networks provide a more efficient method for predicting future egg prices by learning from past patterns.