Title: Dimension reduction for time series in a BSS context using R
Authors: Klaus Nordhausen - Vienna University of Technology (Austria)
Markus Matilainen - University of Turku/Turku PET Centre (Finland) [presenting]
Jari Miettinen - University of Jyvaskyla (Finland)
Joni Virta - Aalto University (Finland)
Sara Taskinen - University of Jyvaskyla (Finland)
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. We present 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. Examples are provided to illustrate the functionality of the package.