Optimizing predictor variables in artificial neural networks when forecasting raw material prices for energy production

This paper applies a heuristic approach to optimize the predictor variables in artificial neural networks when forecasting raw material prices for energy production (coking coal, natural gas, crude oil and coal) to achieve a better forecast. Two goals are (1) to determine the optimum number of time-delayed terms or past values forming the lagged variables and (2) to improve the forecast accuracy by adding intrinsic signals to the lagged variables. The conclusions clearly are in opposition to the actual scientific literature: when addressing the lagged variable size, the results do not confirm relationships among their size, representativeness and estimation accuracy. It is also possible to verify an important effect of the results on the lagged variable size. Finally, adding the order in the time series of the lagged variables to form the predictor variables improves the forecast accuracy in most cases.