Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy

Abstract

This paper describes an empirical investigation aimed at measuring the effect of different initial values and loss functions (both symmetric and asymmetric) on the post-sample forecasting accuracy. The 1001 series of the M-competition are used and three exponential smoothing methods are employed. The results are compared over various types of data and forecasting horizons and validated with additional data. The paper concludes that contrary to expectations, post-sample forecasting accuracies are not affected by the type of initial values used or the loss function employed in the great majority of cases.