Fig. 5: The receiver operating characteristic (ROC) curves showing performance of indicators in the experimental and empirical data using the amplitude adjusted Fourier transform (AAFT) surrogate method.
From: Predicting critical transitions with machine learning trained on surrogates of historical data
The ROC curves show the SDML classifier (SDML, purple), variance (Var, orange), and lag-1 autocorrelation (AC, green) for the A chick heart aggregates going through a period-doubling bifurcation; B sediment data from the MS21 core, C MS66 core, and (D) 64PE406E1 core showing rapid transitions to an anoxic state in the Mediterranean Sea; E ice core records showing rapid paleoclimate transitions; and F transitions in construction activity in pre-Hispanic Pueblo societies. Performance is assessed on the number of experimental runs (N) for each dataset, with 40 equally spaced predictions made between 60% and 100% (A, B, E, F) or between 80% and 100% (C, D) of the way through the pre-transition data. The area under the curve (AUC) score, denoted by A, is a performance measure. The insets show the proportion of predictions made by the classifier for true pre-transition trajectories. “Pre-tran” means close to a critical transition, and “Neutral” means far from a critical transition. The diagonal dashed black line marks where a classifier works no better than a random coin toss, namely, AUC is 0.5.
