Catastrophe prediction via estimated network autocorrelation

Abstract

Described is a system for catastrophe prediction. The system generates a time series of observables at multiple time steps from data observed from a complex system. A surrogate time series based on the time series of observables is then generated. Inferred network structures for both the time series of observables and the surrogate time series are reconstructed. Next, spatial autocorrelation for each inferred network structure in both the time series of observables and the surrogate time series is computed. A statistical test of a detected trend between the time series of observables and the surrogate time series is computed to determine if the detected trend occurred by chance. Finally, an early warning signal of the detected trend occurring by chance is generated.