  We predict the emergence of extreme events in a parametrically driven
nonlinear dynamical system using three Deep Learning models, namely Multi-Layer
Perceptron, Convolutional Neural Network and Long Short-Term Memory. The Deep
Learning models are trained using the training set and are allowed to predict
the test set data. After prediction, the time series of the actual and the
predicted values are plotted one over the other in order to visualize the
performance of the models. Upon evaluating the Root Mean Square Error value
between predicted and the actual values of all three models, we find that the
Long Short-Term Memory model can serve as the best model to forecast the
chaotic time series and to predict the emergence of extreme events for the
considered system.
