Bayesian hierarchical modelling of sparse count processes in retail analytics
Bayesian hierarchical modelling of sparse count processes in retail analytics
复制标题
零售分析中稀疏计数过程的贝叶斯分层建模
DOI：
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发表时间：
2018
影响因子：
1.8
通讯作者：
Gordon J. Ross
中科院分区：
文献类型：
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作者：
J. Pitkin;I. Manolopoulou;Gordon J. Ross
丰富数据的可用性已经改变了零售分析领域，这些数据可用于执行需求预测和库存管理等任务。然而，事实证明更具挑战性的一项任务是预测销量很少的产品的需求。所得数据的稀疏性限制了传统分析的部署程度。为了解决这个问题，我们将销售数据表示为结构化稀疏多元点过程，该过程允许自相关、互相关和时间聚类等特征，这些特征已知存在于稀疏销售数据中。我们引入了贝叶斯点过程模型来捕获这些现象，其中包括一个处理稀疏性的障碍组件和一个处理产品内部和跨产品的时间聚类的令人兴奋的组件。然后，我们将该模型放入贝叶斯分层框架内，以允许跨不同产品借用信息，这是解决每个产品的数据稀疏性的关键。我们使用真实销售数据进行详细分析，表明该模型在预测能力方面优于现有方法，并讨论了推论的解释。
The field of retail analytics has been transformed by the availability of rich data which can be used to perform tasks such as demand forecasting and inventory management. However, one task which has proved more challenging is the forecasting of demand for products which exhibit very few sales. The sparsity of the resulting data limits the degree to which traditional analytics can be deployed. To combat this, we represent sales data as a structured sparse multivariate point process which allows for features such as auto-correlation, cross-correlation, and temporal clustering, known to be present in sparse sales data. We introduce a Bayesian point process model to capture these phenomena, which includes a hurdle component to cope with sparsity and an exciting component to cope with temporal clustering within and across products. We then cast this model within a Bayesian hierarchical framework, to allow the borrowing of information across different products, which is key in addressing the data sparsity per product. We conduct a detailed analysis using real sales data to show that this model outperforms existing methods in terms of predictive power and we discuss the interpretation of the inference.