Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-4906
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWildi, Marc-
dc.contributor.authorMcElroy, Tucker-
dc.date.accessioned2018-11-30T08:14:07Z-
dc.date.available2018-11-30T08:14:07Z-
dc.date.issued2016-
dc.identifier.issn2194-6507de_CH
dc.identifier.issn1941-1928de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13385-
dc.description.abstractThe classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the data-generating process into an uncorrelated white noise process. By design, this universal decomposition is indifferent to particular features of a specific prediction problem (e. g., forecasting or signal extraction) – or features driven by the priorities of the data-users. A single optimization principle (one-step ahead forecast error minimization) is proposed by this classical paradigm to address a plethora of prediction problems. In contrast, this paper proposes to reconcile prediction problem structures, user priorities, and optimization principles into a general framework whose scope encompasses the classic approach. We introduce the linear prediction problem (LPP), which in turn yields an LPP objective function. Then one can fit models via LPP minimization, or one can directly optimize the linear filter corresponding to the LPP, yielding the Direct Filter Approach. We provide theoretical results and practical algorithms for both applications of the LPP, and discuss the merits and limitations of each. Our empirical illustrations focus on trend estimation (low-pass filtering) and seasonal adjustment in real-time, i. e., constructing filters that depend only on present and past data.de_CH
dc.language.isoende_CH
dc.publisherDe Gruyterde_CH
dc.relation.ispartofJournal of time series econometricsde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subject.ddc003: Systemede_CH
dc.titleOptimal real-time filters for linear prediction problemsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1515/jtse-2014-0019de_CH
dc.identifier.doi10.21256/zhaw-4906de_CH
zhaw.funding.euNode_CH
zhaw.issue2de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume8de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
Appears in Collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2016_Wildi_Optimal_real_time_filters_for_linear_prediction_problems.pdf545.29 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.