Methods and Algorithms for Robust Filtering

dc.contributor.author	Fried, Roland	de
dc.contributor.author	Gather, Ursula	de
dc.date.accessioned	2004-12-06T18:39:35Z
dc.date.available	2004-12-06T18:39:35Z
dc.date.issued	2004	de
dc.description.abstract	We discuss filtering procedures for robust extraction of a signal from noisy time series. Moving averages and running medians are standard methods for this, but they have shortcomings when large spikes (outliers) respectively trends occur. Modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Improvements can be achieved by using robust regression methods, which work even in real time because of increased computational power and faster algorithms. Extending recent work we present filters for robust online signal extraction and discuss their merits for preserving trends, abrupt shifts and extremes and for the removal of spikes.	en
dc.format.extent	125547 bytes
dc.format.extent	284759 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4915
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-6821
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	signal extraction	en
dc.subject	drift	en
dc.subject	edge	en
dc.subject	outlier	en
dc.subject	update algorithm	en
dc.subject.ddc	310	de
dc.title	Methods and Algorithms for Robust Filtering	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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