Hierarchical dynamic modelling for individualized Bayesian forecasting
本文提出大规模分层动态模型，用于超市销售中家庭层面购买行为的个性化预测，以支持定价和促销决策。模型为全贝叶斯、可解释且多尺度，通过聚合到个体的信息流提升预测精度。
Abstract We present a case study and methodological developments in large-scale hierarchical dynamic modelling for personalized prediction in commerce. The context is supermarket sales, where improved forecasting of household-specific purchasing behaviour informs decisions about personalized pricing and promotions. This setting involves many thousands of heterogeneous customers and items. Models developed are fully Bayesian, interpretable and multi-scale, with hierarchical forms overlaid on the inherent structure of the retail setting. Customer behavior is modelled at several levels of aggregation, and information flows from aggregate to individual levels. Methodological innovations include extensions of Bayesian dynamic mixture models, their integration into multi-scale systems, and forecast evaluation with context-specific metrics. The use of simultaneous predictors from multiple hierarchical levels improves forecasts at the customer-item level of main interest.