Filtering the Noise from Time Series and Spatial Data

dc.contributor.author	Berke, Olaf	de
dc.date.accessioned	2004-12-06T18:38:58Z
dc.date.available	2004-12-06T18:38:58Z
dc.date.issued	1998	de
dc.description.abstract	Noisy 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.extent	154635 bytes
dc.format.extent	164932 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4881
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-14542
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	geostatistics	en
dc.subject	Kalman Filter	en
dc.subject	kriging	en
dc.subject	prediction	en
dc.subject	signal	en
dc.subject	time series analysis	en
dc.subject.ddc	310	de
dc.title	Filtering the Noise from Time Series and Spatial Data	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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