Over the years, families of forecasting models, such as the exponential smoothing family and Autoregressive Integrated Moving Average, have expanded to contain multiple possible forms and forecasting profiles. In this paper, we question the need to consider such large families of models. We argue that parsimoniously identifying suitable subsets of models will not decrease the forecasting accuracy nor will it reduce the ability to estimate the forecast uncertainty. We propose a framework that balances forecasting performance versus computational cost, resulting in a set of reduced families of models and empirically demonstrate this trade-offs. We translate computational benefits to monetary cost savings and discuss the implications of our results in the context of large retailers.

翻译：多年来,预测模型(如指数式平滑的家庭和自动递减综合移动平均值)的家属已经扩大,包括了多种可能的形式和预测概况。在本文件中,我们质疑是否需要考虑如此庞大的模型群。我们争辩说,巧妙地确定适当的模型子集不会降低预测准确性,也不会降低预测不确定性的能力。我们提出了一个平衡预测业绩和计算成本的框架,导致一系列模型的减少,并用经验来证明这种权衡。我们把计算效益转化为货币成本节约,并讨论我们的结果对大型零售商的影响。