Hierarchical dynamic modelling for individualized Bayesian forecasting
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.
Duke Scholars
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0104 Statistics
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0104 Statistics