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

arXiv: https://arxiv.org/abs/2107.08819 · HTML: https://mlsanity.com/paper/2107.08819/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Machine learning prediction of critical transition and system collapse](https://mlsanity.com/paper/2012.01545/) (2020)
- [Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems](https://mlsanity.com/paper/2002.05514/) (2020)
- [Rapid Time Series Prediction with a Hardware-Based Reservoir Computer](https://mlsanity.com/paper/1807.07627/) (2018)
- [Machine learning assisted quantum state estimation](https://mlsanity.com/paper/2003.03441/) (2020)
- [Separation of Chaotic Signals by Reservoir Computing](https://mlsanity.com/paper/1910.10080/) (2019)
- [Identification of Chimera using Machine Learning](https://mlsanity.com/paper/2001.08985/) (2020)
- [Using Machine Learning to predict extreme events in the H'enon map](https://mlsanity.com/paper/2002.10268/) (2020)
- [Machine Learning assisted Chimera and Solitary states in Networks](https://mlsanity.com/paper/2011.01135/) (2020)

## Built on by (most cited first, 3 of 3)

- [Model-assisted deep learning of rare extreme events from partial observations](https://mlsanity.com/paper/2111.04857/) (2021)
- [Learning unidirectional coupling using echo-state network](https://mlsanity.com/paper/2303.13562/) (2023)
- [Prediction of Occurrence of Extreme Events using Machine Learning](https://mlsanity.com/paper/2110.09304/) (2021)

## Abstract (arXiv)

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.