Tree-based Learning for High-Fidelity Prediction of Chaos
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916822806167552 |
|---|---|
| author | Giammarese, Adam Rana, Kamal Bollt, Erik M. Malik, Nishant |
| author_facet | Giammarese, Adam Rana, Kamal Bollt, Erik M. Malik, Nishant |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13836 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tree-based Learning for High-Fidelity Prediction of Chaos Giammarese, Adam Rana, Kamal Bollt, Erik M. Malik, Nishant Machine Learning Dynamical Systems Chaotic Dynamics Data Analysis, Statistics and Probability 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. |
| title | Tree-based Learning for High-Fidelity Prediction of Chaos |
| topic | Machine Learning Dynamical Systems Chaotic Dynamics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2403.13836 |