TY  - JOUR
VL  - 18
SP  - 946
A1  - Pitkin, James
A1  - Manolopoulou, Ioanna
A1  - Ross, Gordon
JF  - The Annals of Applied Statistics
PB  - Institute of Mathematical Statistics
Y1  - 2024/06//
N2  - 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 autocorrelation, 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.
ID  - discovery10191992
UR  - http://dx.doi.org/10.1214/23-aoas1811
N1  - This version is the version of record. For information on re-use, please refer to the publisher?s terms and conditions.
KW  - Cross-excitation 
KW  -  demand forecasting 
KW  -  Hawkes process 
KW  -  hurdle model 
KW  -  intermittent demand 
KW  -  self-excitation 
KW  -  slow-moving-inventory
TI  - Bayesian hierarchical modelling of sparse count processes in retail analytics
SN  - 1932-6157
AV  - public
IS  - 2
EP  - 965
ER  -