Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm

The iron and steel industry plays a fundamental role in a country's national economy, especially in developing countries. China is the largest iron ore consumption market in the world. However, because of limited domestic iron ore resources, a large proportion of iron ore is imported from other countries. Faced with the conflict between the iron ore supply shortage and the growing demand, it is necessary for the government to predict imports and total consumption. This paper develops a high-precision hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm. We use the China Statistical Yearbook (1996–2011) as our database to test the efficiency and accuracy of the proposed method. According to the experimental results, the proposed new method clearly can improve the prediction accuracy of the original grey model. Future projections have also been done for iron ore imports and total consumption in China in the next five years.