The algorithms available for retail forecasting have increased in complexity. Newer methods, such as machine learning, are inherently complex. The more traditional families of forecasting models, such as exponential smoothing and autoregressive integrated moving averages, have expanded to contain multiple possible forms and forecasting profiles. We question complexity in forecasting and the need to consider such large families of models. Our argument is that parsimoniously identifying suitable subsets of models will not decrease forecasting accuracy nor will it reduce the ability to estimate forecast uncertainty. We propose a framework that balances forecasting performance versus computational cost, resulting in the consideration of only a reduced set of models. We empirically demonstrate that a reduced set performs well. Finally, we translate computational benefits to monetary cost savings and environmental impact and discuss the implications of our results in the context of large retailers.

翻译：用于零售预测的算法日益复杂。较新的方法（如机器学习）本质上是复杂的。更传统的预测模型族，例如指数平滑法和自回归积分滑动平均模型，已扩展至包含多种可能的形式和预测配置文件。我们对预测中的复杂性以及考虑如此庞大的模型族的必要性提出质疑。我们的论点是，简约地识别合适的模型子集不会降低预测准确性，也不会削弱估计预测不确定性的能力。我们提出了一个平衡预测性能与计算成本的框架，从而只需考虑一个简化的模型集。我们通过实证表明，精简后的模型集表现良好。最后，我们将计算收益转化为货币成本节约和环境影响，并讨论了我们研究结果在大型零售商背景下的意义。