Filtering the Noise from Time Series and Spatial Data

dc.contributor.authorBerke, Olafde
dc.date.accessioned2004-12-06T18:38:58Z
dc.date.available2004-12-06T18:38:58Z
dc.date.issued1998de
dc.description.abstractNoisy observations form the basis for almost every scientific research and especially in environmental monitoring. The Noise is often an effect of imprecise instruments which cause measurement errors. If the noise variance is known it is possible to filter out the contaminating noise from the observations and then to predict the latent signal process. Solutions for this problem exist for time series application and will be briefly reviewed. In the geostatistical literature, i.e. for the analysis of spatial data, similar methods have been foreshadowed in the literature and will be outlined in this work.en
dc.format.extent154635 bytes
dc.format.extent164932 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/4881
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14542
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectgeostatisticsen
dc.subjectKalman Filteren
dc.subjectkrigingen
dc.subjectpredictionen
dc.subjectsignalen
dc.subjecttime series analysisen
dc.subject.ddc310de
dc.titleFiltering the Noise from Time Series and Spatial Dataen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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