The Impact of Dynamic Shocks and Special Days on Time Series Data

dc.authorscopusid 58961022200
dc.authorscopusid 7005981141
dc.contributor.author Gökdağ,Z.H.
dc.contributor.author Bilge, Ayşe Hümeyra
dc.contributor.author Bilge,A.H.
dc.contributor.other Industrial Engineering
dc.date.accessioned 2024-06-23T21:38:39Z
dc.date.available 2024-06-23T21:38:39Z
dc.date.issued 2023
dc.department Kadir Has University en_US
dc.department-temp Gökdağ Z.H., Kadir Has University, Faculty of Eng. and Natural Sciences, Depart. of Industrial Engineering, İstanbul, 34083, Turkey; Bilge A.H., Kadir Has University, Faculty of Eng. and Natural Sciences, Depart. of Industrial Engineering, İstanbul, 34083, Turkey en_US
dc.description.abstract This paper includes an examination of a 4-year time series data on retail delivery demand generated by a logistics company based on the dates of creation. The periodic fluctuations observed in the data's normal structure are caused by the accumulation of demands over the weekend and their fulfillment at the beginning of the week. The aim of the study is modeling the response to unexpected changes in demand, which we refer to as "shocks," similar to the weekend effect. Special days, including single-day public holidays, religious holidays, and campaign periods in November, which represent specific periods, were also analyzed to interpret the patterns during these periods. The patterns created by single-day public holidays and religious holidays are significantly influenced by whether these days fall on a weekend or a weekday. By excluding weeks with special days from the overall data, the presence of shock effects in the remaining ordinary weeks was examined. During this period, the shock caused by the Covid-19 pandemic and adverse weather conditions was observed. The impact of the Covid-19 shock lasted longer compared to other shocks. When the increase in demand due to shocks exceeds the capacity of existing vehicles, the problem can be resolved by arranging daily rental vehicles from companies that provide vehicle allocations. Extracting the demand model for special days and unexpected shocks will ensure operational preparedness and prevent process delays. When ordinary weeks were examined, a monotonically decreasing trend from Monday to Sunday was observed based on the weekly average demand. The maximum demand was 58.3% on Monday, 17.2% on Tuesday, 15.9% on Wednesday, 7.3% on Thursday, and 1.3% on Friday. The provided graphs also demonstrate a significant increase in demands in early 2020 due to the widespread adoption of e-commerce as a result of the Covid-19 pandemic. © IJCESEN. en_US
dc.description.sponsorship Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.22399/ijcesen.1311166
dc.identifier.endpage 190 en_US
dc.identifier.issn 2149-9144
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-85188968795
dc.identifier.startpage 183 en_US
dc.identifier.trdizinid 1182938
dc.identifier.uri https://doi.org/10.22399/ijcesen.1311166
dc.identifier.uri https://hdl.handle.net/20.500.12469/5818
dc.identifier.volume 9 en_US
dc.language.iso en en_US
dc.publisher Prof.Dr. İskender AKKURT en_US
dc.relation.ispartof International Journal of Computational and Experimental Science and Engineering en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 2
dc.subject Covid-19 en_US
dc.subject Demand en_US
dc.subject Dynamic shock en_US
dc.subject Pattern en_US
dc.subject Time series data en_US
dc.title The Impact of Dynamic Shocks and Special Days on Time Series Data en_US
dc.type Article en_US
dspace.entity.type Publication
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relation.isOrgUnitOfPublication.latestForDiscovery 28868d0c-e9a4-4de1-822f-c8df06d2086a

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