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Related Experiment Video

Updated: Oct 10, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events.

Arnob Ray¹, Tanujit Chakraborty², Dibakar Ghosh¹

¹Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India.

Chaos (Woodbury, N.Y.)

|December 9, 2021

Summary

This study introduces an optimized ensemble deep learning (OEDL) model, combining multiple neural networks for superior forecasting of complex dynamics and extreme events. The OEDL framework enhances accuracy and stability for predicting chaotic systems and real-world phenomena.

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Last Updated: Oct 10, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Physics
Computer Science
Data Science

Background:

Deep learning and ensemble methods are powerful tools for analyzing physical phenomena.
These techniques are traditionally used independently, limiting their synergistic potential.
Forecasting unpredictable chaotic dynamics, especially extreme events, presents significant scientific challenges.

Purpose of the Study:

To develop an optimized ensemble deep learning (OEDL) framework integrating deep learning and ensemble methods.
To achieve synergistic improvements in accuracy, stability, scalability, and reproducibility for dynamic system predictions.
To advance the forecasting of nonlinear systems, with a specific focus on predicting extreme events.

Main Methods:

Developed an optimized ensemble deep learning (OEDL) framework.
Employed a best convex combination of feed-forward neural networks, reservoir computing, and long short-term memory (LSTM).
Validated the framework on numerically simulated data and real-world datasets, including chaotic systems, epidemiological data, and climate data.

Main Results:

The OEDL framework demonstrated superior out-of-sample performance compared to individual deep learners and standard ensemble methods.
Achieved significant improvements in model accuracy, stability, scalability, and reproducibility.
Successfully forecasted extreme events from a Liénard-type system, COVID-19 cases in Brazil, dengue cases in San Juan, and sea surface temperature in the Niño 3.4 region.

Conclusions:

The proposed OEDL framework offers a powerful approach for synergistic improvements in forecasting complex dynamics.
This integrated methodology is highly effective for predicting extreme events in both simulated and real-world scenarios.
The OEDL model represents a significant advancement in applying machine learning for dynamic system prediction.