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.

预测是几乎所有科学研究的关键步骤，在工业、商业、临床和经济活动的许多领域都是必不可少的。文献中有很多预测方法；但指数平滑因其简单性和准确性而脱颖而出。尽管指数平滑被广泛使用并且在文献中已有很长时间的事实，但它存在一些可能影响模型预测准确性的问题。最近提出了一种称为 Ata 的替代预测框架来克服这些问题并提供改进的预测。在本研究中，将在无趋势或线性趋势的数据集之间比较 Ata 和指数平滑的预测精度。本研究的结果是使用具有不同样本量、方差的模拟数据集获得的。在短期和长期预测范围内比较预测误差。结果表明，在预测近期和遥远的未来时，所提出的方法对于两种类型的时间序列数据都优于指数平滑法。这些方法在美国服务数据的年化月利率上实施，并且还针对该数据集比较了它们的预测性能。