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

TL;DR

This paper introduces an optimized ensemble deep learning framework that combines multiple models to improve the accuracy and stability of forecasting chaotic dynamics and extreme events in various real-world and simulated datasets.

Contribution

The study develops a joint ensemble deep learning framework that synergistically integrates feed-forward neural networks, reservoir computing, and LSTM for enhanced extreme event prediction.

Findings

Outperforms individual deep learners and standard ensembles in accuracy.

Effective in predicting extreme events in diverse datasets.

Demonstrates superior out-of-sample performance on real-world data.

Abstract

The remarkable flexibility and adaptability of both deep learning models and ensemble methods have led to the proliferation for their application in understanding many physical phenomena. Traditionally, these two techniques have largely been treated as independent methodologies in practical applications. This study develops an optimized ensemble deep learning (OEDL) framework wherein these two machine learning techniques are jointly used to achieve synergistic improvements in model accuracy, stability, scalability, and reproducibility prompting a new wave of applications in the forecasting of dynamics. Unpredictability is considered as one of the key features of chaotic dynamics, so forecasting such dynamics of nonlinear systems is a relevant issue in the scientific community. It becomes more challenging when the prediction of extreme events is the focus issue for us. In this circumstance, the proposed OEDL model based on a best convex combination of feed-forward neural networks, reservoir computing, and long short-term memory can play a key role in advancing predictions of dynamics consisting of extreme events. The combined framework can generate the best out-of-sample performance than the individual deep learners and standard ensemble framework for both numerically simulated and real world data sets. We exhibit the outstanding performance of the OEDL framework for forecasting extreme events generated from Lienard-type system, prediction of COVID-19 cases in Brazil, dengue cases in San Juan, and sea surface temperature in Nino 3.4 region.