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We predict the emergence of extreme events in a parametrically drivennonlinear dynamical system using three Deep Learning models, namely Multi-LayerPerceptron, Convolutional Neural Network and Long Short-Term Memory. The DeepLearning models are trained using the training set and are allowed to predictthe test set data. After prediction, the time series of the actual and thepredicted values are plotted one over the other in order to visualize theperformance of the models. Upon evaluating the Root Mean Square Error valuebetween predicted and the actual values of all three models, we find that theLong Short-Term Memory model can serve as the best model to forecast thechaotic time series and to predict the emergence of extreme events for theconsidered system.

Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learning