On the number of signals in multivariate time series

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


  • Markus Matilainen
  • Klaus Nordhausen
  • Joni Virta

Research units

  • University of Turku
  • Turku PET Centre
  • Vienna University of Technology


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.


Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 14th International Conference, LVA/ICA 2018, Proceedings
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Latent Variable Analysis and Signal Separation - Guildford, United Kingdom
Duration: 2 Jul 20185 Jul 2018
Conference number: 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10891 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Latent Variable Analysis and Signal Separation
Abbreviated titleLVA/ICA
CountryUnited Kingdom
Internet address

ID: 29768274