Tree-based Learning for High-Fidelity Prediction of Chaos
<|reference_start|>Tree-based Learning for High-Fidelity Prediction of Chaos: 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.<|reference_end|>
@article{giammarese2024tree-based,
    title={Tree-based Learning for High-Fidelity Prediction of Chaos},
    author={Adam Giammarese, Kamal Rana, Erik M. Bollt, Nishant Malik},
    journal={arXiv preprint arXiv:2403.13836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13836},
    primaryClass={cs.LG math.DS nlin.CD physics.data-an stat.ML}
}