Repository logo
 
Publication

Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?

dc.contributor.authorRamos, Patricia
dc.contributor.authorOliveira, José Manuel
dc.contributor.authorKourentzes, Nikolaos
dc.contributor.authorFildes, Robert
dc.date.accessioned2023-01-25T09:13:23Z
dc.date.available2023-01-25T09:13:23Z
dc.date.issued2022
dc.description.abstractRetailers depend on accurate forecasts of product sales at the Store SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model’s parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/asi6010003pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/21839
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.ispartofseries1;
dc.subjectRetailingpt_PT
dc.subjectForecastingpt_PT
dc.subjectPromotionspt_PT
dc.subjectSeasonalitypt_PT
dc.subjectShrinkagept_PT
dc.subjectPrincipal components analysispt_PT
dc.titleForecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage21pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleApplied System Innovationpt_PT
oaire.citation.volume6pt_PT
person.familyNameRamos
person.givenNamePatricia
person.identifierR-000-E03
person.identifier.ciencia-id5E16-0270-BC7F
person.identifier.orcid0000-0002-0959-8446
person.identifier.ridB-2728-2017
person.identifier.scopus-author-id7103233146
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication774272fa-abef-4aca-8c70-7b874ccf79fa
relation.isAuthorOfPublication.latestForDiscovery774272fa-abef-4aca-8c70-7b874ccf79fa

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
asi-06-00003.pdf
Size:
558.99 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: