Robust online signal extraction from multivariate time series

Vivian Lanius and Ursula Gather

Computational Statistics & Data Analysis, 2010, vol. 54, issue 4, 966-975

Abstract: Robust regression-based online filters for multivariate time series are proposed and their performance in real time signal extraction settings is discussed. The focus is on methods that can deal with time series exhibiting trends, level changes, outliers and a high level of noise as well as periods of a comparatively steady state. The new filter is based on a robust two-step online procedure, and it recognises that the data are often measured on a discrete scale. The relevant properties and the performance of this new filter are discussed and investigated by means of simulations and by a medical application.

Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00379-X
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:54:y:2010:i:4:p:966-975

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().