Goto

Collaborating Authors

 treedox

Tree-based Learning for High-Fidelity Prediction of Chaos

Giammarese, Adam, Rana, Kamal, Bollt, Erik M., Malik, Nishant

arXiv.org Machine Learning

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.

2403.13836
  Country:
  Genre: Research Report (0.83)
  Technology:

Add feedback