Using Neural Networks for Predicting Futures Contract Prices of White Maize in South Africa

Abstract

The growing ability to collect and integrate data from disparate sources on a larger scale creates new opportunities for improved decision making. The concepts of using data for predictions by using statistical or computational intelligence models have been researched extensively. However, the tools and techniques that make it possible to analyse the data in real-time as it is created brings about additional opportunities for discovering useful patterns and timely actionable insights. The prices of agricultural grain commodities are known to be volatile due to several factors that influence the prices of grain commodities. Moreover, different combinations of these factors are responsible for the price volatility at different times.