Robust online signal extraction from multivariate time series

dc.contributor.authorGather, Ursula
dc.contributor.authorLanius, Vivian
dc.date.accessioned2008-11-26T14:17:33Z
dc.date.available2008-11-26T14:17:33Z
dc.date.issued2008-11-26T14:17:33Z
dc.description.abstractWe 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.en
dc.identifier.urihttp://hdl.handle.net/2003/25862
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-12768
dc.language.isoende
dc.subjectMultivariate time seriesen
dc.subjectOnline methodsen
dc.subjectRobust regressionen
dc.subjectSignal extractionen
dc.subject.ddc004
dc.titleRobust online signal extraction from multivariate time seriesen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
tr38-Lanius.pdf
Size:
674.09 KB
Format:
Adobe Portable Document Format
Description:
DNB
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.92 KB
Format:
Item-specific license agreed upon to submission
Description: