Linear combination of forecasts with numerical adjustment via MINIMAX non-linear programming

Linear combination of forecasts with numerical adjustment via MINIMAX non-linear programming

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This paper proposes a linear combination of forecasts obtained from three forecasting methods (namely, ARIMA, Exponential Smoothing and Artificial Neural Networks) whose adaptive weights are determined via a multi-objective non-linear programming problem, which seeks to minimize, simultaneously, the Doormat statistics: MAE, MAPE and MSE.The results achieved by the proposed combination are compared with the traditional approach of linear combinations of forecasts, where the optimum adaptive weights are determined only by minimizing the MSE; Heart Rate Handlebar Left with the combination method by arithmetic mean; and with individual methods.