Robust online signal extraction from multivariate time series
Vivian Lanius and
Ursula Gather
No 2007,38, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen
Abstract:
We introduce robust regression-based online filters for multivariate time series and discuss their performance in real time signal extraction settings. We focus on methods that can deal with time series exhibiting patterns such as trends, level changes, outliers and a high level of noise as well as periods of a rather steady state. In particular, the data may be measured on a discrete scale which often occurs in practice. Our new filter is based on a robust two-step online procedure. We investigate its relevant properties and its performance by means of simulations and a medical application.
Keywords: Multivariate time series; signal extraction; robust regression; online methods (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.econstor.eu/bitstream/10419/36580/1/600049035.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:sfb475:200738
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 ().