eprintid: 10191992
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/19/92
datestamp: 2024-05-10 08:34:11
lastmod: 2024-05-10 08:34:11
status_changed: 2024-05-10 08:34:11
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Pitkin, James
creators_name: Manolopoulou, Ioanna
creators_name: Ross, Gordon
title: Bayesian hierarchical modelling of sparse count processes in retail analytics
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
divisions: F61
keywords: Cross-excitation , demand forecasting , Hawkes process , hurdle model , intermittent demand , self-excitation , slow-moving-inventory
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: 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.
date: 2024-06
date_type: published
publisher: Institute of Mathematical Statistics
official_url: http://dx.doi.org/10.1214/23-aoas1811
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2272014
doi: 10.1214/23-AOAS1811
lyricists_name: Manolopoulou, Ioanna
lyricists_id: IMANO09
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
funding_acknowledgements: [EPSRC]; [Alan Turing Institute]; [dunnhumby ltd]
full_text_status: public
publication: The Annals of Applied Statistics
volume: 18
number: 2
pagerange: 946-965
pages: 20
issn: 1932-6157
citation:        Pitkin, James;    Manolopoulou, Ioanna;    Ross, Gordon;      (2024)    Bayesian hierarchical modelling of sparse count processes in retail analytics.                   The Annals of Applied Statistics , 18  (2)   pp. 946-965.    10.1214/23-AOAS1811 <https://doi.org/10.1214/23-AOAS1811>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10191992/1/23-AOAS1811.pdf