Tree-based Learning for High-Fidelity Prediction of Chaos
Adam Giammarese Kamal Rana Erik M. Bollt Nishant Malik
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.