Model-free prognostication of non-linear time series
Fig 5
Feature-space plots and local Lyapunov exponents.
A,B) The data for South Africa were analyzed. The panels on the left display the feature-space plots for the raw data. In the middle panels, ac + 1 and ami were scaled up by multiplication with the half-maximal value of new cases during the sliding window durations. In the right panels, the values for the new cases were divided by their maximum during the observation window. A) Feature-space plots. Shown are the pairwise feature-space plots for average mutual information (ami), autocorrelation (ac), and new cases (7-day moving average per million inhabitants) (nc), comprising string lengths of 150 days and time lags of 15 days. B) Lyapunov exponents over time. In the upper panel, the individual Lyapunov characteristic exponents are shown in comparison to the suitably scaled new cases over time, for each pair of readouts (i.e., pairwise among nc = new cases, ac = autocorrelation, ami = average mutual information). The red trace indicates the normalized new cases per day. The bottom panel displays the maximum Lyapunov exponents (MLE) over time (blue line) in comparison to the normalized new cases per day (red line).