Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-15034-0_13 | Computational Methods in Systems Biology |
Keywords | DocType | ISSN |
Early warning signals, Probabilistic programming, Complex systems | Conference | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ville Laitinen | 1 | 0 | 0.34 |
| Leo Lahti | 2 | 1 | 0.77 |