Tree-based Learning for High-Fidelity Prediction of Chaos

2024/03/12 by Giammarese, Adam, Rana, Kamal, Bollt, Erik M. +1 · 1 citation
#Chaotic Dynamics (nlin.CD) #Data Analysis #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics and Probability (physics.data-an)

paper · doi:10.48550/arxiv.2403.13836

Abstract

Model-free forecasting of the temporal evolution of chaotic systems is crucial but challenging. Existing solutions require hyperparameter tuning, significantly hindering their wider adoption. In this work, we introduce a tree-based approach not requiring hyperparameter tuning: TreeDOX. It uses time delay overembedding as explicit short-term memory and Extra-Trees Regressors to perform feature reduction and forecasting. We demonstrate the state-of-the-art performance of TreeDOX using the Henon map, Lorenz and Kuramoto-Sivashinsky systems, and the real-world Southern Oscillation Index.

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