Combining forecasts using recursive equal weighting and linear programming

Two combining methods, called recursive equal weighting (REW) and linear programming (LP), respectively, are introduced. Their forecasting performance is compared with other combining methods. The proposed REW method not only reaches a forecasting accuracy comparable to that of the theoretically optimal combining methods, but also provides some valuable insights into the methodology of combination, i.e., the combination of combined forecasts can further improve the forecasting performance. The occurrence of outliers is shown to lead to inaccuracy in the ordinary least squares (OLS) solution, while the proposed LP method can effectively eliminate their effects