Abstract
Fast and robust methods are needed for denoising time series data measured with high sampling frequencies. In intensive care e.g. physiological variables like the heart rate are observed in short time intervals. Systematic changes have to be detected quickly and distinguished from clinically irrelevant short term fluctuations and artifacts. Median filtering works well if there is no substantial trend in the data but improvements are possible by approximating the data by a local linear trend.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Davies, P.L., Fried, R. and Gather, U. (2002). Robust signal extraction for on-line monitoring data. Technical Report 1/2002, SFB 475, University of Dortmund, Germany.
Siegel, A.F. (1982). Robust regression using repeated medians. Biometrika,68, 242–244.
Sposito, V.A. (1990). Some properties of L p -Estimators. In: K.D. Lawrence and J.L. Arthur (eds.). Robust Regression. Analysis and Applications, 23–58. New York: Marcel Dekker.
Stromberg, A.J. (1993). Computing the exact least median of squares estimate and stability diagnostics in multiple linear regression. SIAM J. Sci. Comput., 14, 1289–1299.
Tukey, J.W. (1977). Exploratory Data Analysis. Reading, Mass.: Addison-Wesley.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fried, R., Gather, U. (2002). Fast and Robust Filtering of Time Series with Trends. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-57489-4_54
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1517-7
Online ISBN: 978-3-642-57489-4
eBook Packages: Springer Book Archive