Comparison of forecast accuracy of Ata and exponential smoothing

Forecasting is a crucial step in almost all scientific research and is essential in many areas of industrial, commercial, clinical and economic activity. There are many forecasting methods in the literature; but exponential smoothing stands out due to its simplicity and accuracy. Despite the facts that exponential smoothing is widely used and has been in the literature for a long time, it suffers from some problems that potentially affect the model's forecast accuracy. An alternative forecasting framework, called Ata, was recently proposed to overcome these problems and to provide improved forecasts. In this study, the forecast accuracy of Ata and exponential smoothing will be compared among data sets with no or linear trend. The results of this study are obtained using simulated data sets with different sample sizes, variances. Forecast errors are compared within both short and long term forecasting horizons. The results show that the proposed approach outperforms exponential smoothing for both types of time series data when forecasting the near and distant future. The methods are implemented on the U.S. annualized monthly interest rates for services data and their forecasting performance are also compared for this data set.