Filtering the noise from time series and spatial data

Olaf Berke

No 1998,18, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

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.

Keywords: Geostatistics; Kalman Filter; Kriging; Prediction; Signal; Time Series Analysis (search for similar items in EconPapers)
Date: 1998
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/77152/2/1998-18.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:199818

Access Statistics for this paper

More papers in Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().