Comparison of forecasting methodologies using egg price as a test case

Poultry Science
H A Ahmad, M Mariano

Abstract

Egg price forecasting of shelled eggs is a complex problem. Traditionally, future egg price has been predicted using a combination of regression analysis and experienced-based intuition to build a model, which is then fine-tuned to prevalent market conditions. Even after collecting reliable and expensive data, the subsequent analysis, in many cases, does not produce a high confidence to explain the variations in egg price. In the current project, a different approach using neural networks was used to forecast egg price. A neural network is a mathematical model of an information-processing structure that is loosely based on our present understanding of the working of human brain. An artificial neural network consists of a large number of simple processing elements connected to each other in a network. Urner Barry egg quotes from 1991 to 2002 as well as number of hens, egg storage capacity, and number of eggs placed for hatching from the USDA databases (1993 to 2000) were used to forecast egg price. Regression analysis explained only 37% of the variation in egg price due to the above-mentioned 3 factors. Neural networks, on the other hand, recognize the pattern in previous years' egg prices and then predict the future price more ...Continue Reading

Citations

Jun 5, 2012·The Journal of Applied Poultry Research·H A Ahmad

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