Dimension Reduction for Time Series in a Blind Source Separation Context Using R
Nordhausen, K., Matilainen, M., Miettinen, J., Virta, J., & Taskinen, S. (2021). Dimension Reduction for Time Series in a Blind Source Separation Context Using R. Journal of Statistical Software, 98, Article 15. https://doi.org/10.18637/jss.v098.i15
Published inJournal of Statistical Software
© Authors, 2021
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.
PublisherFoundation for Open Access Statistic
Publication in research information system
MetadataShow full item record
Additional information about fundingThe work of KN was supported by the CRoNoS COST Action IC1408 and the Austrian Science Fund P31881-N32. The work of ST was supported by the CRoNoS COST Action IC1408. The work of JV was supported by Academy of Finland (grant 321883).
Showing items with similar title or keywords.
Pan, Yan; Matilainen, Markus; Taskinen, Sara; Nordhausen, Klaus (John Wiley & Sons, 2022)Second order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly ...
Muehlmann, C.; De Iaco, S.; Nordhausen, K. (Springer Science and Business Media LLC, 2023)With advances in modern worlds technology, huge datasets that show dependencies in space as well as in time occur frequently in practice. As an example, several monitoring stations at different geographical locations track ...
Nordhausen, Klaus; Taskinen, Sara; Virta, Joni (IEEE, 2022)Many modern multivariate time series datasets contain a large amount of noise, and the first step of the data analysis is to separate the noise channels from the signals of interest. A crucial part of this dimension reduction ...
Helske, Jouni (University of Jyväskylä, 2015)A large amount of data collected today is in the form of a time series. In order to make realistic inferences based on time series forecasts, in addition to point predictions, prediction intervals or other measures of ...
Voutilainen, Miikka (University of Jyväskylä, 2016)