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dc.contributor.authorWildi, Marc-
dc.date.accessioned2019-04-17T14:21:23Z-
dc.date.available2019-04-17T14:21:23Z-
dc.date.issued2018-
dc.identifier.isbn978-92-79-80170-9de_CH
dc.identifier.isbn978-92-79-80169-3de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/16664-
dc.description.abstractSignal extraction concerns the definition, the analysis and the extraction of systematic patterns in time series. We here rely on linear filters and propose a new phenomenological approach which emphasizes filter effects. In this perspective, optimal designs are derived by tuning filter characteristics to application purposes. In essence, our approach aligns optimization criteria on problem structures and user priorities. We stress the importance of an agnostic approach whose scope generalizes classical filters as well as traditional model-based approaches. In particular, we propose customized criteria for minimizing revisions and for emphasizing timeliness and/or reliability of early (real-time) estimates. The key towards our customized approach is a thorough analysis of filter effects in the frequency domain. We identify filters with transfer functions and decompose the filter effect into amplitude and phase errors. In a real-time perspective, the resulting decomposition of the mean-square filter error enables to track simultaneously reliability/accuracy issues (noise suppression) as well as timeliness (time-shift/delay) aspects. The resulting optimality concept blends with the structure of the estimation problem and the intention of the analyst, as well. We here propose a new generalized optimization criterion which bridges the gap between the original Direct Filter Approach (DFA) and a numerically fast linear approximation I-DFA of the former. Unlike I-DFA, the resulting new estimation method is able to replicate the original DFA perfectly and it is almost as fast, in computational terms, as I-DFA.de_CH
dc.language.isoende_CH
dc.publisherEurostatde_CH
dc.relation.ispartofHandbook on seasonal adjustmentde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleReal time trend extraction and seasonal adjustmentde_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeLuxembourgde_CH
dc.identifier.doi10.2785/279605de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end451de_CH
zhaw.pages.start415de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
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Wildi, M. (2018). Real time trend extraction and seasonal adjustment. In Handbook on seasonal adjustment (pp. 415–451). Eurostat. https://doi.org/10.2785/279605
Wildi, M. (2018) ‘Real time trend extraction and seasonal adjustment’, in Handbook on seasonal adjustment. Luxembourg: Eurostat, pp. 415–451. Available at: https://doi.org/10.2785/279605.
M. Wildi, “Real time trend extraction and seasonal adjustment,” in Handbook on seasonal adjustment, Luxembourg: Eurostat, 2018, pp. 415–451. doi: 10.2785/279605.
WILDI, Marc, 2018. Real time trend extraction and seasonal adjustment. In: Handbook on seasonal adjustment. Luxembourg: Eurostat. S. 415–451. ISBN 978-92-79-80170-9
Wildi, Marc. 2018. “Real Time Trend Extraction and Seasonal Adjustment.” In Handbook on Seasonal Adjustment, 415–51. Luxembourg: Eurostat. https://doi.org/10.2785/279605.
Wildi, Marc. “Real Time Trend Extraction and Seasonal Adjustment.” Handbook on Seasonal Adjustment, Eurostat, 2018, pp. 415–51, https://doi.org/10.2785/279605.


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