The University of Southampton
University of Southampton Institutional Repository

Optimal autoregressive modelling of a measured noisy time series using singular value decomposition

Shin, K., Feraday, S.A., Harris, C.J., Brennan, M.J. and Oh, J.-E. (2003) Optimal autoregressive modelling of a measured noisy time series using singular value decomposition Mechanical Systems and Signal Processing, 17, (2), pp. 423-432. (doi:10.1006/mssp.2002.1510).

Record type: Article


A new simple method using singular-value decomposition (SVD) to find the optimal order for an autoregressive (AR) model of a deterministic time series is proposed. The method is particularly effective when the signal is contaminated with additive noise, and it is shown that the choice of sampling rate is also important when the signal is contaminated with noise. In this paper, the signal of interest is the impulse response of a second-order differential system, and various levels of white noise are also added to the signal, to show the robustness of the method. Simulation results show the method to be very reliable even when the noise level is high (e.g. a signal-to-noise ratio of 6 dB). To validate the method on experimental data the method is applied to the impulse response of a cantilever beam contaminated with additive white noise.

Full text not available from this repository.

More information

Published date: 2003


Local EPrints ID: 10073
ISSN: 0888-3270
PURE UUID: aff0a979-833c-412f-8f08-8fc834a0b669

Catalogue record

Date deposited: 18 Feb 2005
Last modified: 17 Jul 2017 17:08

Export record



Author: K. Shin
Author: S.A. Feraday
Author: C.J. Harris
Author: M.J. Brennan
Author: J.-E. Oh

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.