Show simple item record

dc.contributor.authorLeong, Kelvin
dc.contributor.authorSung, Anna
dc.date.accessioned2023-10-23T09:15:59Z
dc.date.available2023-10-23T09:15:59Z
dc.date.issued2023-10-23
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/628216/comovement%20clustering.pdf?sequence=1
dc.identifier.citationLeong K., & Sung A. (2023). Co-movement clustering: A novel approach for predicting inflation in the food and beverage industry. Journal of Event, Tourism and Hospitality Studies, 3, 1-21. https://doi.org/10.32890/jeth2023.3.1en_US
dc.identifier.doi10.32890/jeth2023.3.1
dc.identifier.urihttp://hdl.handle.net/10034/628216
dc.description.abstractIn the realm of food and beverage businesses, inflation poses a significant hurdle as it affects pricing, profitability, and consumer’s purchasing power, setting it apart from other industries. This study proposes a novel approach; co-movement clustering, to predict which items will be inflated together according to historical time-series data. Experiments were conducted to evaluate the proposed approach based on real-world data obtained from the UK Office for National Statistics. The predicted results of the proposed approach were compared against four classical methods (correlation, Euclidean distance, Cosine Similarity, and DTW). According to our experimental results, the accuracy of the proposed approach outperforms the above-mentioned classical methods. Moreover, the accuracy of the proposed approach is higher when an additional filter is applied. Our approach aids hospitality operators in accurately predicting food and beverage inflation, enabling the development of effective strategies to navigate the current challenging business environment in hospitality management. The lack of previous work has explored how time series clustering can be applied to support inflation prediction. This study opens a new research paradigm to the related field and this study can serve as a useful reference for future research in this emerging area. In addition, this study work contributes to the data analytics research stream in hospitality management literature.en_US
dc.publisherUniversiti Utara Malaysiaen_US
dc.relation.urlhttps://e-journal.uum.edu.my/index.php/jeth/article/view/19222en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectSustainabilityen_US
dc.subjectTechnologyen_US
dc.subjectData Analyticsen_US
dc.subjectFinTechen_US
dc.subjectAnalyticsen_US
dc.subjectHospitalityen_US
dc.subjectClusteringen_US
dc.subjectInnovationen_US
dc.subjectFood and Beverageen_US
dc.subjectTime series analysisen_US
dc.subjectInflationen_US
dc.titleCo-movement clustering: A novel approach for predicting inflation in the food and beverage industryen_US
dc.typeArticleen_US
dc.identifier.eissn2805-4423en_US
dc.contributor.departmentUniversity of Chesteren_US
dc.identifier.journalJournal of Event, Tourism and Hospitality Studiesen_US
or.grant.openaccessYesen_US
rioxxterms.funderN/Aen_US
rioxxterms.identifier.projectN/Aen_US
rioxxterms.versionVoRen_US
rioxxterms.versionofrecord10.32890/jeth2023.3.1en_US
dcterms.dateAccepted2023-08-06
rioxxterms.publicationdate2023-10-23
dc.date.deposited2023-10-23en_US


Files in this item

Thumbnail
Name:
comovement clustering.pdf
Size:
451.2Kb
Format:
PDF
Request:
Article - VoR

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International