A4 Article in conference proceedings
On the number of signals in multivariate time series




List of Authors: Markus Matilainen, Klaus Nordhausen, Joni Virta
Publisher: Springer Verlag
Publication year: 2018
Journal: Lecture Notes in Computer Science
Book title *: Latent Variable Analysis and Signal Separation
Journal name in source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Title of series: Lecture Notes in Computer Science
Volume number: 10891
ISBN: 978-3-319-93763-2
eISBN: 978-3-319-93764-9
ISSN: 0302-9743

Abstract

We assume a second-order source separation model where the observed
multivariate time series is a linear mixture of latent, temporally
uncorrelated time series with some components pure white noise. To avoid
the modelling of noise, we extract the non-noise latent components
using some standard method, allowing the modelling of the extracted
univariate time series individually. An important question is the
determination of which of the latent components are of interest in
modelling and which can be considered as noise. Bootstrap-based methods
have recently been used in determining the latent dimension in various
methods of unsupervised and supervised dimension reduction and we
propose a set of similar estimation strategies for second-order
stationary time series. Simulation studies and a sound wave example are
used to show the method’s effectiveness.


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Last updated on 2019-19-07 at 19:46