Comparison of NNARX, ANN and ARIMA Techniques to Poultry Retail Price Forecasting

The lack of study among the economic forecasting literature that can empirically proves the hypothesis of being more powerfulness of dynamic neural networks in comparison with the static neural networks models for forecasting, is the most important motivation of this study. In this paper, the utilization of NNARX as a nonlinear dynamic neural network model, ANN as a nonlinear static neural network model and ARIMA as a linear model were compared to forecast poultry retail price. As a case study on Iranian poultry retail price, we compare forecast performance of these models for three forecasts (1, 2 and 4 week ahead). Results show that NNARX and ANN models outperform ARIMA model, and also NNARX model outperforms ANN model for all three forecasts.


Issue Date:
2009
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/50321
Total Pages:
12
Series Statement:
Contributed Paper
374




 Record created 2017-04-01, last modified 2017-08-25

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