Predicting critical transitions in dynamical systems from time series using nonstationary probability density modeling
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
American Physical Society
This is the final version of the article. Available from the American Physical Society via the DOI in this record.
A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent. © 2013 American Physical Society.
Physical Review E, 2013, 88, 052917