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

TL;DR

This paper demonstrates that deep learning models, especially LSTM, can effectively predict the emergence of extreme events in a nonlinear dynamical system, providing a promising approach for forecasting chaos-induced phenomena.

Contribution

The study compares three deep learning models and identifies LSTM as the most effective for predicting extreme events in a parametrically driven nonlinear system.

Findings

LSTM outperforms MLP and CNN in prediction accuracy.

Deep learning models can forecast chaotic extreme events.

LSTM achieves the lowest RMSE among tested models.

Abstract

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.