Repository logo
 
Publication

Improving forecasting by resampling STL decomposition

2023-06-30Conference object Restricted access
Simple item page
datacite.subject.sdg13:Ação Climáticapt_PT
datacite.subject.sdg17:Parcerias para a Implementação dos Objetivospt_PT
dc.contributor.authorCordeiro, Clara
dc.contributor.authorRamos, Maria do Rosário
dc.contributor.authorNeves, M. Manuela
dc.date.accessioned2023-07-24T09:02:58Z
dc.date.available2023-07-24T09:02:58Z
dc.date.issued2023-06-30
dc.description.abstractThe development of new forecasting algorithms has shown an increasing interest due to the emerging of new fields of application like machine learning and forecasting competitions. Although initially intended for independent random variables, bootstrap methods can be successfully applied to time series. The Boot.EXPOS procedure, which combines bootstrap and exponential smoothing methods, has shown promising results for forecasting. This work proposes a new approach to forecasting, which is briefly described as follows: using Seasonal-Trend decomposition by Loess (STL), the best STL fit is selected by testing all possible combinations of parameters. The best combination of smoothing parameters is chosen based on an accuracy measure. The time series is then decomposed into components according to the best STL fit. The Boot.EXPOS procedure is employed to forecast the seasonal component and the seasonally adjusted time series. These forecasts are aggregated to obtain a final forecast. The performance of this combined forecast is evaluated using real datasets and compared with other established forecasting methods.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.citationCordeiro, C., Ramos, M.R., Neves, M.M. (2023). Improving Forecasting by Resampling STL Decomposition. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14112. Springer, Cham. https://doi.org/10.1007/978-3-031-37129-5_12pt_PT
dc.identifier.doi10.1007/978-3-031-37129-5_12pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.2/14571
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationCentre of Statistics and its Applications
dc.subjectBoot.EXPOSpt_PT
dc.subjectForecastpt_PT
dc.subjectTime seriespt_PT
dc.subjectSeasonal-Trend decomposition by Loesspt_PT
dc.titleImproving forecasting by resampling STL decompositionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleCentre of Statistics and its Applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00006%2F2020/PT
oaire.citation.conferencePlaceAthens, Greecept_PT
oaire.citation.endPage149pt_PT
oaire.citation.startPage140pt_PT
oaire.citation.titleComputational Science and Its Applications – ICCSA 2023 Workshops. Lecture Notes in Computer Sciencept_PT
oaire.citation.volume14112pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameRamos
person.givenNameMaria do Rosário
person.identifier206757
person.identifier.ciencia-id3A1F-E648-078D
person.identifier.orcid0000-0001-9114-0807
person.identifier.ridP-4530-2015
person.identifier.scopus-author-id54403639500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationc6cdb7b7-607d-4775-947f-d153ff6dded2
relation.isAuthorOfPublication.latestForDiscoveryc6cdb7b7-607d-4775-947f-d153ff6dded2
relation.isProjectOfPublication200da949-b304-425e-8937-4221c1b2f32b
relation.isProjectOfPublication.latestForDiscovery200da949-b304-425e-8937-4221c1b2f32b

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
(provas CAS)550355_1_En_12_Chapter_Author(Cordeiro_Ramos_Neves).pdf
Size:
423.17 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.97 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Matemática e Estatística | Comunicações em congressos, conferências e seminários / Communications in congresses, conferences