Filtering the Noise from Time Series and Spatial Data
Loading...
Date
1998
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Universitätsbibliothek Dortmund
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.
Description
Table of contents
Keywords
geostatistics, Kalman Filter, kriging, prediction, signal, time series analysis