Tree-based Learning for High-Fidelity Prediction of Chaos

• URL: http://arxiv.org/abs/2403.13836v1
• Date: Tue, 12 Mar 2024 01:16:29 GMT
• Title: Tree-based Learning for High-Fidelity Prediction of Chaos
• Authors: Adam Giammarese, Kamal Rana, Erik M. Bollt, Nishant Malik,
• Abstract summary: TreeDOX is a tree-based approach to model-free forecasting of chaotic systems. 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.
• Score: 0.2999888908665658
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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