Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory

Price forecasting is a vital matter for mining investment decisions, as it represents the credibility of any financial outcome claimed by the feasibility studies presented to investors in financial markets. Most of these financial studies use forecasts from well-known providers of price assessments and market data that, ultimately, constitute a black box for the investors. This is why, to achieve credibility, user-friendly forecasting techniques through which the future price instability can be bounded are needed. This paper examines the performance in forecasting coking coal prices of traditional time series models, ROBUST models, ARIMA models, generalized regression neural networks (GRNNs) and multi-layer feedforward networks (MLFNs). Two cases are analysed: (1) considering the full time series, and (2) considering the trans genic time series. The latter is based on a theory that addresses the existence of raw material price cycles and the presence of anomalous phenomena due to periods with spiked prices, and it eliminates these periods from the time series to improve the accuracy of the forecasts. At this point, the question of which forecasting performance measure provides a better projection of the errors becomes critical. Keeping in mind that we are focusing on commodity prices and that these results are going to be used for estimating incomes, percentual deviations represent a greater compromise for the forecasts. Among the RMSE, MAE and MAPE, the mean absolute percentage error (MAPE) is the most representative since it is the only one that considers percentual deviations. On the other hand, since the error does not follow a normal distribution, the MAE can be considered to be the second-most representative performance measure, and the RMSE is third. When the full time series is used for the forecast, the GRNN model gives more accurate forecasts than the MLFN model, the ARIMA model, the traditional time series models and the ROBUST model. On the other hand, when the transgenic time series is used for the forecast, the performance of the GRNN model is improved, but, in this case, the ARIMA model achieves a more accurate forecast.