核心概念
TreeDOX, a tree-based approach, offers accurate and user-friendly chaos prediction without hyperparameter tuning.
摘要
The content introduces TreeDOX, a novel tree-based method for forecasting chaos. It compares favorably to existing models like LSTM and NG-RC in accuracy and efficiency. The methodology involves time delay overembedding and Extra Trees Regression for feature reduction and forecasting. Results on various chaotic systems like the H´enon map, Lorenz system, Kuramoto-Sivashinsky equation, and real-world Southern Oscillation Index demonstrate the effectiveness of TreeDOX. Additional insights are provided through visualizations and experiments showcasing its performance.
Structure:
- Introduction to TreeDOX
- Comparison with Existing Models
- Application on Chaotic Systems
- Supplemental Material: Visualizations and Experiments
統計資料
Model-free approaches represent a significant breakthrough in modeling complex systems.
Deep learning techniques like RNNs and LSTMs provide substantial performance in forecasting chaotic systems.
Next Generation Reservoir Computing (NG-RC) converts RC into a mathematically equivalent nonlinear vector autoregression machine.
ETRs have advantages over RFs in terms of time complexity, variance, bias, training complexity, and predictions.
引述
"Model-free approaches represent a significant breakthrough in modeling complex systems."
"Deep learning techniques such as Recurrent Neural Networks (RNN) provide substantial performance in forecasting chaotic systems."
"ETRs have two advantages over RFs: lower time complexity due to randomized splits and lower bias due to lack of bootstrapping."