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Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning Models

2024Conference paper Restricted access
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datacite.subject.fosCiências Sociais::Economia e Gestão
datacite.subject.sdg12:Produção e Consumo Sustentáveis
dc.contributor.authorVaz, Clara B.
dc.contributor.authorSena, Inês
dc.contributor.authorBraga, Ana Cristina
dc.contributor.authorNovais, Paulo
dc.contributor.authorLima, José
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2025-04-23T10:19:09Z
dc.date.available2025-04-23T10:19:09Z
dc.date.issued2024
dc.description.abstractRetail transactions represent sales of consumer goods, or final goods, by consumer companies. This sector faces security challenges due to the hustle and bustle of sales, affecting employees’ workload. In this context, it is essential to estimate the number of customers who will appear in the store daily so that companies can dynamically adjust employee schedules, aligning workforce capacity with expected demand. This can be achieved by forecasting transactions using past observations and forecasting algorithms. This study aims to compare the ARIMA time series algorithm with several Machine Learning algorithms to predict the number of daily transactions in different store patterns, considering data variability. The study identifies four typical store patterns based on these criteria using daily transaction data between 2019 and 2023 from all retail stores of the leading company in Portugal. Due to data variability and the results obtained, the algorithm that presents the most minor errors in predicting daily transactions is selected for each store. This study’s ultimate goal is to fill the gap in forecasting daily customer transactions and present a suitable forecasting model to mitigate risks associated with transactions in retail stores.eng
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDB/05757/2020), and SusTEC LA/P/0007/2021 (DOI: 10.54499/LA/P/0007/2020). This work has been supported by NORTE-01-0247-FEDER-072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector. Inês Sena was supported by FCT PhD grant UI/BD/153348/2022.
dc.identifier.citationVaz, Clara Bento; Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Lima, José; Pereira, Ana I. (2024). Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning Models. In Optimization, Learning Algorithms and Applications (OL2A 2024), Part I. Cham: Springer Nature. p. 268- 283. eISBN 978-3-031-77426-3
dc.identifier.doi10.1007/978-3-031-77426-3_18
dc.identifier.isbn9783031774256
dc.identifier.isbn9783031774263
dc.identifier.urihttp://hdl.handle.net/10198/34426
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationIntelligent system for occupational safety in retail sector
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization, Learning Algorithms and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectForecasting
dc.subjectTransactions
dc.subjectTime series
dc.subjectMachine Learning
dc.titlePredicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning Modelseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleIntelligent system for occupational safety in retail sector
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F153348%2F2022/PT
oaire.citation.endPage283
oaire.citation.startPage268
oaire.citation.title4th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2024
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamOE
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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