Why and how does exponential smoothing fail? An in depth comparison of ATA-simple and simple exponential smoothing.

Even though exponential smoothing (ES) is publicized as one of the most successful forecasting methods in the time series literature and it is widely used in practice due to its simplicity, its accuracy can be affected by the initialization and optimization procedures followed. It also suffers from some fundamental problems that can be seen clearly when its weighting scheme is studied closely. Exponential smoothing fails to account for the number of data points that can contribute to the forecast when assigning weights to historical data. ATA smoothing has been proposed as an alternative forecasting method and is shown to perform better than ES when the accuracies are compared on empirical data. In this paper, the properties of ATA that make it stand out from ES models will be discussed by just comparing the simple versions of both models. Empirical performance of the two simple models will also be compared based on popular error metrics.