A1 Journal article (refereed)
Blind recovery of sources for multivariate space-time random fields (2023)

Muehlmann, C., De Iaco, S., & Nordhausen, K. (2023). Blind recovery of sources for multivariate space-time random fields. Stochastic Environmental Research and Risk Assessment, 37(4), 1593-1613. https://doi.org/10.1007/s00477-022-02348-2

JYU authors or editors

Publication details

All authors or editorsMuehlmann, C.; De Iaco, S.; Nordhausen, K.

Journal or seriesStochastic Environmental Research and Risk Assessment



Publication year2023

Publication date30/12/2022


Issue number4

Pages range1593-1613

PublisherSpringer Science and Business Media LLC

Publication countryGermany

Publication languageEnglish


Research data linkhttps://www.scottishairquality.scot/data

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/85097


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 hourly concentration measurements of a number of air pollutants for several years. Such a dataset contains thousands of multivariate observations, thus, proper statistical analysis needs to account for dependencies in space and time between and among the different monitored variables. To simplify the consequent multivariate spatio-temporal statistical analysis it might be of interest to detect linear transformations of the original observations that result in straightforward interpretative, spatio-temporally uncorrelated processes that are also highly likely to have a real physical meaning. Blind source separation (BSS) represents a statistical methodology which has the aim to recover so-called latent processes, that exactly meet the former requirements. BSS was already successfully used in sole temporal and sole spatial applications with great success, but, it was not yet introduced for the spatio-temporal case. In this contribution, a reasonable and innovative generalization of BSS for multivariate space-time random fields (stBSS), under second-order stationarity, is proposed, together with two space-time extensions of the well-known algorithms for multiple unknown signals extraction (stAMUSE) and the second-order blind identification (stSOBI) which solve the formulated problem. Furthermore, symmetry and separability properties of the model are elaborated and connections to the space-time linear model of coregionalization and to the classical principal component analysis are drawn. Finally, the usefulness of the new methods is shown in a thorough simulation study and on a real environmental application.

Keywordstime seriestime-series analysisgeographic informationspatial analysisgeostatisticsmultivariable methodssignal processing

Contributing organizations

Ministry reportingYes

Reporting Year2022

JUFO rating1

Last updated on 2024-30-04 at 17:56