Predicting discrete-time bifurcations with deep learning.

2 (a–e)), we observe an increasing trend in variance (Fig. 2 f–j), and an increasing or decreasing trend in lag-1 autocorrelation (Fig. 2 k–o).

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Performance of EWS on chick heart data In the chick heart data, we mostly observe an increasing trend in variance and a decreasing trend in lag-1 autocorrelation prior to the period-doubling bifurcation, as previously reported 21 .

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Variance and lag-1 autocorrelation are considered to provide an EWS if they display a strong trend, which we quantify using the Kendall tau statistic.

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