Neural network models for time series forecasts
Management Science
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Network Models: Theory and Projects
Neural Network Models: Theory and Projects
LearningWeights for Linear Combination of Forecasting Methods
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Forecasting demand of commodities after natural disasters
Expert Systems with Applications: An International Journal
Adaptive neuro-fuzzy inference system for combined forecasts in a panel manufacturer
Expert Systems with Applications: An International Journal
Combining linear and nonlinear model in forecasting tourism demand
Expert Systems with Applications: An International Journal
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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.