Skip to Main content Skip to Navigation
Journal articles

Statistical early-warning indicators based on autoregressive moving-average models

Abstract : We address the problem of defining early-warning indicators of critical transitions. To this purpose, we fit the relevant time series through a class of linear models, known as autoregressive moving-average (ARMA(p, q)) models. We define two indicators representing the total order and the total persistence of the process, linked, respectively, to the shape and to the characteristic decay time of the autocorrelation function of the process. We successfully test the method to detect transitions in a Langevin model and a 2D Ising model with nearest-neighbor interaction. We then apply the method to complex systems, namely for dynamo thresholds and financial crisis detection.
Document type :
Journal articles
Complete list of metadatas

Cited literature [12 references]  Display  Hide  Download
Contributor : Dominique Girard <>
Submitted on : Tuesday, October 18, 2016 - 5:12:28 PM
Last modification on : Wednesday, July 1, 2020 - 2:44:04 PM


Files produced by the author(s)




Davide Faranda, Bérengère Dubrulle, Flavio Maria Emanuele Pons. Statistical early-warning indicators based on autoregressive moving-average models. Journal of Physics A: Mathematical and Theoretical, IOP Publishing, 2014, 47, pp.252001. ⟨10.1088/1751-8113⟩. ⟨cea-01383485⟩



Record views


Files downloads