Comparison of Combined And Conventional Models in Forecasting Prices of Wheat, Corn and Sugar

Document Type : Research Paper

Authors

• R. M
• M. J.R

Abstract

Reliable predictions on import prices of agricultural products could result in appropriate timing of import and saving of exchange resources. Linear time series models including ARIMA, GARCH and EGARCH have frequently been used in time series forecasting during the past three decades. Recent studies on forecasting with artificial neural network (ANNs) suggest that ANNs can be a suitable alternative to the traditional linear models. But neither linear time series models nor ANNs could be adequate in modeling and forecasting time series since the linear model cannot deal with nonlinear relationships while neural network model alone is not able to handle both linear and nonlinear patterns equally well. Hence, by integrating linear time series with ANN models and designing the hybrid model, patterns in the data could be explained more accurately. In this research, a hybrid methodology that combines time series model ARIMA, GARCH and EGARCH and ANN models is designed and results are compared with those of competitive models. In order to compare forecasting accuracy, in addition to the usual criteria such as RMSE, MAD, MAPE and Theil Coefficient with introducing Granger and Newbold statistic, significance of forecasting accuracy have been investigated. Findings for prices of wheat, corn and sugar indicated that hybrid model significantly improves forecasting accuracy. In order to save foreign exchange, application of hybrid models for agricultural price prediction is recommended.

Keywords

• Linear Models
• Artificial Neural Networks
• Time Series Forecasting
• Combining Models

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