Forecasting Agricultural Commodity Prices using Hybrid Neural Networks

Abstract

Traditionally, autoregressive integrated moving average (ARIMA) models have been one of the most widely used linear models in time series forecasting. However, ARIMA models can not easily capture nonlinear patterns. In the last two decades artificial neural networks (ANNs) have been proposed as an alternative to traditional linear models, particularly in the presence of nonlinear data patterns. Recent research suggests that a hybrid approach combining both ARIMA models and ANNs can lead to further improvements in the forecasting accuracy compared with pure models. In this paper, a hybrid model that combines a seasonal ARIMA model and an Elman neural network (ENN) is used to forecast agricultural commodity prices. Different approaches for specifying the ANNs are investigated among others, and a genetic algorithm (GA) is employed to determine the optimal architecture of the ANNs. It turns out that the out-of-sample prediction can be improved slightly with the hybrid model.