Robust detail-preserving signal extraction

dc.contributor.authorFried, Roland
dc.contributor.authorGather, Ursula
dc.contributor.authorLanius, Vivian
dc.date.accessioned2005-12-14T09:10:08Z
dc.date.available2005-12-14T09:10:08Z
dc.date.issued2005-12-14T09:10:08Z
dc.description.abstractWe discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends occur. Modifications like modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Better solutions can be based on robust regression techniques, which even work in real time because of increased computational power and faster algorithms. Reviewing previous work we present filters for robust signal extraction and discuss their merits for preserving trends, abrupt shifts and local extremes as well as for the removal of outliers.en
dc.format.extent197537 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2003/21757
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15406
dc.language.isoen
dc.subjectAbruft shiften
dc.subjectLinear median hybrid filteren
dc.subjectNoisy time serieen
dc.subjectOutlieren
dc.subjectRobust filtering procedureen
dc.subjectSignal extractionen
dc.subject.ddc004
dc.titleRobust detail-preserving signal extractionen
dc.typeText
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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