Cetin Beyza, Yavuz Idil
Department of Statistics, School of Science, Dokuz Eylul University, Izmir, Turkey.
J Appl Stat. 2020 Aug 10;48(13-15):2580-2590. doi: 10.1080/02664763.2020.1803813. eCollection 2021.
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.
预测几乎是所有科学研究中的关键步骤，并且在工业、商业、临床和经济活动的许多领域都至关重要。文献中有许多预测方法；但指数平滑法因其简单性和准确性而脱颖而出。尽管指数平滑法被广泛使用且在文献中存在已久，但它存在一些可能影响模型预测准确性的问题。最近提出了一种名为Ata的替代预测框架，以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和指数平滑法的预测准确性。本研究的结果是使用具有不同样本大小、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，在预测近期和远期未来时，所提出的方法在两种类型的时间序列数据上均优于指数平滑法。这些方法应用于美国服务业年化月度利率数据，并针对该数据集比较了它们的预测性能。
