# Prediction of Occurrence of Extreme Events using Machine Learning

arXiv: https://arxiv.org/abs/2110.09304 · HTML: https://mlsanity.com/paper/2110.09304/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events](https://mlsanity.com/paper/2106.08968/) (2021)
- [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)
- [Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learning](https://mlsanity.com/paper/2107.08819/) (2021)

## Built on by (most cited first, 1 of 1)

- [Noise-Induced Predictability Redistribution Across Forecast Horizons of Extreme Events in Chaotic Dynamics](https://mlsanity.com/paper/2609.27877/) (2026)

## Abstract (arXiv)

Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occurrence of extreme events in a nonlinear mechanical system. Extreme events are rare events that occur ubiquitously in nature. We consider four machine learning models, namely Logistic Regression, Support Vector Machine, Random Forest and Multi-Layer Perceptron in our prediction task. We train these four machine learning models using training set data and compute the performance of each model using the test set data. We show that the Multi-Layer Perceptron model performs better among the four models in the prediction of extreme events in the considered system. The persistent behaviour of the considered machine learning models is cross-checked with randomly shuffled training set and test set data.