Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Klaus Nordhausen, Markus Matilainen, Jari Miettinen, Joni Virta, Sara Taskinen
Title: Dimension Reduction for Time Series in a Blind Source Separation Context Using R
Abstract: Multivariate time series observations are increasingly common in multiple fields of science but the complex dependencies of such data often translate into intractable models with large number of parameters. An alternative is given by first reducing the dimension of the series and then modelling the resulting uncorrelated signals univariately, avoiding the need for any covariance parameters. A popular and effective framework for this is blind source separation. In this paper we review the dimension reduction tools for time series available in the R package tsBSS. These include methods for estimating the signal dimension of second-order stationary time series, dimension reduction techniques for stochastic volatility models and supervised dimension reduction tools for time series regression. Several examples are provided to illustrate the functionality of the package.

Page views:: 391. Submitted: 2018-10-11. Published: 2021-07-12.
Paper: Dimension Reduction for Time Series in a Blind Source Separation Context Using R     Download PDF (Downloads: 129)
Supplements:
tsBSS_1.0.0.tar.gz: R source package Download (Downloads: 9; 71KB)
v98i15.R: R replication code Download (Downloads: 14; 9KB)
PRSA_data_2010.1.1-2014.12.31.csv: Supplementary data (CSV format) Download (Downloads: 10; 1MB)

DOI: 10.18637/jss.v098.i15

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.