LAUSR

Abstract The well-established forecasting methods of exponential smoothing rely on the “optimal” estimation of parameters if they are to perform well. A grid search procedure to minimise the MSE is often used in practice to fit exponential smoothing methods, especially in large inventory control applications. Grid searches are also found in some modern statistical software. We ask whether the ex ante forecast accuracy of the damped trend method of exponential smoothing can be improved by optimising parameters. Furthermore, we ask whether the method should be fitted according to a mean absolute error criterion rather than the mean squared error commonly used in practice. We found that model-fitting matters. Parameter optimization makes significant improvements in forecast accuracy regardless of the fit criterion. We also show that the mean absolute error criterion usually produces better results.