Recently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base models as new observations are newly recorded, and it helps to cope with inaccurate prediction caused by the insufficiency of historical observations. This study compared the properties of several exponentially weighted moving average methods as base models for the self-starting forecasting process. Exponentially weighted moving average methods are the most widely used forecasting techniques because of their superior performance as well as computational efficiency. In this study, we compared the performance of a self-starting forecasting process using different existing exponentially weighted moving average methods under various simulation scenarios and real case datasets. Through this study, we can provide the guideline for determining which exponentially weighted moving average method works best for the self-starting forecasting process.

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自指数预测的指数加权移动平均法比较研究

近来，许多数据分析在许多实际情况下都遭受了历史观测不足的困扰。为了解决历史观测的不足，可以使用自动启动的预测过程。自启动的预测过程会随着新记录的新观测值不断更新基础模型，并有助于应对由于历史观测值不足而导致的不准确预测。这项研究比较了几种指数加权移动平均方法作为自启动预测过程基础模型的特性。指数加权移动平均法由于其优越的性能和计算效率而成为最广泛使用的预测技术。在这个研究中，我们在各种模拟场景和实际案例数据集下，使用不同的现有指数加权移动平均值方法，比较了自启动预测过程的性能。通过这项研究，我们可以为确定哪种指数加权移动平均方法最适合自启动预测过程提供指导。