A major challenge in automating the production of a large number of forecasts, as often required in many business applications, is the need for robust and reliable predictions. Increased noise, outliers and structural changes in the series, all too common in practice, can severely affect the quality of forecasting. We investigate ways to increase the reliability of exponential smoothing forecasts, the most widely used family of forecasting models in business forecasting. We consider two alternative sets of approaches, one stemming from statistics and one from machine learning. To this end, we adapt M-estimators, boosting and inverse boosting to parameter estimation for exponential smoothing. We propose appropriate modifications that are necessary for time series forecasting while aiming to obtain scalable algorithms. We evaluate the various estimation methods using multiple real datasets and find that several approaches outperform the widely used maximum likelihood estimation. The novelty of this work lies in (1) demonstrating the usefulness of M-estimators, (2) and of inverse boosting, which outperforms standard boosting approaches, and (3) a comparative look at statistics versus machine learning inspired approaches.

在许多业务应用程序中经常需要自动生成大量预测，其中的主要挑战是对鲁棒和可靠的预测的需求。在实践中，普遍增加的噪声，离群值和结构变化都可能严重影响预测的质量。我们研究了提高指数平滑预测的可靠性的方法，该指数平滑预测是业务预测中使用最广泛的预测模型系列。我们考虑两种替代方法，一种来自统计数据，另一种来自机器学习。为此，我们将M估计器（增强和逆增强）用于参数估计以进行指数平滑。我们提出了适当的修改，这些修改对于时间序列预测是必需的，同时旨在获得可伸缩的算法。我们使用多个真实数据集评估了各种估算方法，发现几种方法优于广泛使用的最大似然估算。这项工作的新颖之处在于（1）证明了M估计量的有效性；（2）和逆推升的效果优于标准的推升方法；（3）比较了统计数据和受机器学习启发的方法。