Forecast combination in industrial series: A comparison between individual forecasts and its combinations with and without correlated errors

Forecast combination is a method that allows the improvement of accuracy of forecasts. The literature presents several studies that assess the methods of forecast combination existent in relation to its accuracy, but there is no unanimity in the results. The combination method by arithmetic mean is the one most widely used, although some authors consider the minimum variance method as more accurate. The latter allows to consider whether or not the correlation between the errors of individual forecasts, a situation in which is attributed, in this study, the nomenclature of simplified method of minimum variance. This study aims at identifying differences in the accuracy of quantitative forecasts, obtained by these methods. The individual modeling that support the combinations are SARIMA and ANN, and measures of accuracy used to choose the best method are MAPE, MSE and MAE. As the main result, there is a superior performance of the simplified combination method by minimum variance. (c) 2012 Elsevier Ltd. All rights reserved.