Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information
Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information
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DOI：
10.1016/j.ejor.2015.08.029
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Shaohui Ma;R. Fildes;Tao Huang
中科院分区：
文献类型：
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作者：
Shaohui Ma;R. Fildes;Tao Huang
在运筹学中的营销分析应用中，建模师经常面临从大量可能性中选择关键变量的问题。例如，SKU级别的零售店销售额受到类别间和类别内影响，在决定促销策略和编制运营预测时可能需要考虑这些影响。但还没有研究将这一广为接受的概念应用于预测实践：一个明显的障碍是变量空间的超高维。为了解决这一问题，本文提出了一个四步法框架。通过调查类别内和类别间SKU级别促销信息在提高预测准确性方面的价值，说明了这一点。该方法包括识别潜在影响类别，建立解释变量空间，通过多阶段套索回归进行变量选择和模型估计，以及使用滚动方案生成预报。与简化变量空间的其他方法相比，这种新方法在预测精度方面的改进证明了这种新方法在处理高维问题上的成功。实证结果表明，在使用所提出的方法框架时，整合更多信息的模型的表现明显好于基线模型。总体而言，我们可以将预测精度提高12.6%，而只使用SKU自己的预测器。但在取得的改进中，95%来自类别内信息，只有5%来自类别间信息。实质性的营销结果也对促销品类管理产生了影响。
In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts. But no research has yet put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra- and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 12.6 percent over the model using only the SKU's own predictors. But of the improvements achieved, 95 percent of it comes from the intra-category information, and only 5 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.