English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The field of retail analytics has been transformed by the availability ofrich data which can be used to perform tasks such as demand forecasting andinventory management. However, one task which has proved more challenging isthe forecasting of demand for products which exhibit very few sales. Thesparsity of the resulting data limits the degree to which traditional analyticscan be deployed. To combat this, we represent sales data as a structured sparsemultivariate point process which allows for features such as auto-correlation,cross-correlation, and temporal clustering, known to be present in sparse salesdata. We introduce a Bayesian point process model to capture these phenomena,which includes a hurdle component to cope with sparsity and an excitingcomponent to cope with temporal clustering within and across products. We thencast this model within a Bayesian hierarchical framework, to allow theborrowing of information across different products, which is key in addressingthe data sparsity per product. We conduct a detailed analysis using real salesdata to show that this model outperforms existing methods in terms ofpredictive power and we discuss the interpretation of the inference.

Bayesian hierarchical modelling of sparse count processes in retail analytics