A4 Refereed article in a conference publication

Probabilistic Multivariate Early Warning Signals




AuthorsLaitinen Ville, Lahti Leo

EditorsIon Petre, Andrei Păun

Conference nameInternational Conference on Computational Methods in Systems Biology

Publishing placeCham

Publication year2022

JournalLecture Notes in Computer Science

Book title Computational Methods in Systems Biology 20th International Conference, CMSB 2022, Bucharest, Romania, September 14–16, 2022, Proceedings

Series titleLecture Notes in Computer Science

Volume13447

First page 259

Last page274

ISBN978-3-031-15033-3

eISBN978-3-031-15034-0

ISSN0302-9743

eISSN1611-3349

DOIhttps://doi.org/10.1007/978-3-031-15034-0_13(external)

Web address https://link.springer.com/chapter/10.1007/978-3-031-15034-0_13(external)

Preprint addresshttp://arxiv.org/pdf/2205.07576(external)


Abstract

A broad range of natural and social systems from human microbiome to financial markets can go through critical transitions, where the system suddenly collapses to another stable configuration. Anticipating such transition early and accurately can facilitate controlled system manipulation and mitigation of undesired outcomes. Generic data-driven indicators, such as autocorrelation and variance, have been shown to increase in the vicinity of an approaching tipping point, and statistical early warning signals have been reported across a range of systems. In practice, obtaining reliable predictions has proven to challenging, as the available methods deal with simplified one-dimensional representations of complex systems, and rely on the availability of large amounts of data. Here, we demonstrate that a probabilistic data aggregation strategy can provide new ways to improve early warning detection by more efficiently utilizing the available information from multivariate time series. In particular, we consider a probabilistic variant of a vector autoregression model as a novel early warning indicator and argue that it has certain advantages in model regularization, treatment of uncertainties, and parameter interpretation. We evaluate the performance against alternatives in a simulation benchmark and show improved sensitivity in warning signal detection in a common ecological model encompassing multiple interacting species.



Last updated on 2024-26-11 at 13:40