标题： 分层贝叶斯回归用于多地点销售交易预测 显示英文标题

标题： Hierarchical Bayesian Regression for Multi-Location Sales Transaction Forecasting

摘要： 许多预测模型中的特征自然具有层次结构。 较低的层级代表个体或事件。 这些单元自然地分组为地点和时间段或其他聚合体，通常在多个层级上。 分组层级可能相交和合并，就像关系型数据库表一样。 除了表示数据结构外，分层模型中的预测特征可以分配到其适当的层级。 这种模型适合使用分层贝叶斯求解方法，通过将每个组的个体模型与一个将所有组聚合为一个模型的情况进行泛化，从而在组之间“共享”推断结果。 在本文中，我们展示了我们正在进行的工作，将分层贝叶斯模型应用于门店特许经营店全天的购买预测，分组基于地点和一周中的每一天。 我们演示了如何使用\textsf{斯坦}包对一年内收集的单个销售交易数据进行分析。 我们展示了这种方法如何解决数据有限导致每天和每个地点准确性较低的问题，同时能够扩展到大量地点并提高准确性。 显示英文摘要

摘要： The features in many prediction models naturally take the form of a hierarchy. The lower levels represent individuals or events. These units group naturally into locations and intervals or other aggregates, often at multiple levels. Levels of groupings may intersect and join, much as relational database tables do. Besides representing the structure of the data, predictive features in hierarchical models can be assigned to their proper levels. Such models lend themselves to hierarchical Bayes solution methods that ``share'' results of inference between groups by generalizing over the case of individual models for each group versus one model that aggregates all groups into one. In this paper we show our work-in-progress applying a hierarchical Bayesian model to forecast purchases throughout the day at store franchises, with groupings over locations and days of the week. We demonstrate using the \textsf{stan} package on individual sales transaction data collected over the course of a year. We show how this solves the dilemma of having limited data and hence modest accuracy for each day and location, while being able to scale to a large number of locations with improved accuracy.