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 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 in the considered system.

Graphic abstract

中文翻译：

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通过深度学习对参数驱动的非线性动力系统中极端事件的出现进行无模型预测

摘要

我们使用三种深度学习模型（即多层感知器、卷积神经网络和长短期记忆）预测参数驱动的非线性动力系统中极端事件的出现。深度学习模型使用训练集进行训练，并允许预测测试集数据。预测后，将实际值和预测值的时间序列一个一个地绘制在另一个上，以可视化模型的性能。通过评估所有三个模型的预测值和实际值之间的均方根误差值，我们发现长短期记忆模型可以作为预测混沌时间序列和预测极端事件出现的最佳模型。所考虑系统中的事件。

图形摘要