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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578598
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dc.contributor.authorLala Septem Riza
dc.contributor.authorJudhistira Aria Utama
dc.contributor.authorSyandi Mufti Putra
dc.contributor.authorFerry Mukharradi Simatupang
dc.contributor.authorEddy Prasetyo Nugroho
dc.date.accessioned2023-11-06T03:04:14Z-
dc.date.available2023-11-06T03:04:14Z-
dc.date.issued2018-01
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116051
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578598-
dc.descriptionNowadays, large datasets become main intentions of researchers in many areas. However, a challenge that still remains mainly unresolved is the lack of strategies used for analysing large time-series datasets in parallel. Therefore, this research aims to design a model of exponential smoothing working on parallel computing by using the bootstrap method. Three parts will be considered in the model: data preprocessing using the bootstrap methods, parallel exponential smoothing, and aggregation of results to be the final predicted values. To implement the processes, some packages available in the R environment such as “foreach”, “forecast” and “doParallel” are utilised. R environment provides many packages for scientific computing, data analysis, time-series analysis and high performance computing. For testing and validating the proposed model and implementation, a case study in astronomy, i.e. the prediction of asteroid’s orbital elements, was done. Moreover, a comparison and analysis with the results produced by algorithm of Regularized Mix Variable Symplectic 4 Yarkovsky Effect (RMVS4-YE) is also presented in this paper to provide a high level of confidence on the proposed model.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-26-1-1
dc.rightsUKM
dc.subjectExponential smoothing
dc.subjectOrbital element
dc.subjectParallel computing
dc.subjectR programming language
dc.subjectTime series analysis
dc.titleParallel exponential smoothing using the bootstrap method in r for forecasting asteroid’s orbital elements
dc.typeJournal Article
dc.format.volume26
dc.format.pages441-462
dc.format.issue1
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