Publication:
Robust optimization of forecast combinations

dc.contributor.coauthorPost, Thierry
dc.contributor.coauthorArvanitis, Stelios
dc.contributor.departmentDepartment of Business Administration
dc.contributor.kuauthorKarabatı, Selçuk
dc.contributor.kuprofileFaculty Member
dc.contributor.otherDepartment of Business Administration
dc.contributor.schoolcollegeinstituteCollege of Administrative Sciences and Economics
dc.contributor.yokid38819
dc.date.accessioned2024-11-09T11:45:13Z
dc.date.issued2019
dc.description.abstractIn this paper, we introduce RF energy harvesting paradigm into WNCS framework to study the optimal power control, energy harvesting and scheduling problem with the objective of providing maximum level of adaptivity under strict timing and reliability requirements employing the constant rate transmission model. We formulate the problem as a Mixed Integer Linear Programming Problem (MILP). We show the power allocation can be separated from the scheduling and harvesting at optimality. Then, we introduce a heuristic algorithm for the scheduling problem, periodic list scheduling (PLS), inspired from list scheduling of jobs with sequence dependent setup times on identical machines. We then demonstrate via extensive simulations the superiority of the proposed algorithm in terms of closeness to the optimal, adaptivity and runtime.
dc.description.fulltextYES
dc.description.indexedbyWoS
dc.description.indexedbyScopus
dc.description.issue3
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipNazarbayev University
dc.description.sponsorshipResearch Centre of AUEB
dc.description.sponsorshipNational Analytical Center 'Analytica'
dc.description.versionAuthor's final manuscript
dc.description.volume35
dc.formatpdf
dc.identifier.doi10.1016/j.ijforecast.2019.01.007
dc.identifier.eissn1872-8200
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR02854
dc.identifier.issn0169-2070
dc.identifier.linkhttps://doi.org/10.1016/j.ijforecast.2019.01.007
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85065909464
dc.identifier.urihttps://hdl.handle.net/20.500.14288/459
dc.identifier.wos474317500007
dc.keywordsForecast combinations
dc.keywordsStochastic dominance
dc.keywordsAsymptotic theory
dc.keywordsConvex optimization
dc.keywordsVolatility index forecasting
dc.keywordsTime series analysis
dc.languageEnglish
dc.publisherElsevier
dc.relation.grantnoGSB2018003
dc.relation.grantnoEP-2215-01/00-01.
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/9504
dc.sourceInternational Journal of Forecasting
dc.subjectEconomics
dc.subjectManagement
dc.titleRobust optimization of forecast combinations
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.authorid0000-0001-6976-5405
local.contributor.kuauthorKarabatı, Selçuk
relation.isOrgUnitOfPublicationca286af4-45fd-463c-a264-5b47d5caf520
relation.isOrgUnitOfPublication.latestForDiscoveryca286af4-45fd-463c-a264-5b47d5caf520

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