Generative Forecasting with Joint Probability Models for Chaotic Systems
Research Paper#Time Series Forecasting, Generative Models, Chaotic Systems🔬 Research|Analyzed: Jan 3, 2026 09:28•
Published: Dec 30, 2025 20:00
•1 min read
•ArXivAnalysis
This paper addresses the limitations of deterministic forecasting in chaotic systems by proposing a novel generative approach. It shifts the focus from conditional next-step prediction to learning the joint probability distribution of lagged system states. This allows the model to capture complex temporal dependencies and provides a framework for assessing forecast robustness and reliability using uncertainty quantification metrics. The work's significance lies in its potential to improve forecasting accuracy and long-range statistical behavior in chaotic systems, which are notoriously difficult to predict.
Key Takeaways
- •Proposes a generative forecasting approach for chaotic systems.
- •Learns the joint probability distribution of lagged system states.
- •Introduces a model-agnostic training and inference framework.
- •Enables assessment of forecast robustness and reliability using uncertainty quantification metrics.
- •Demonstrates improved performance on Lorenz-63 and Kuramoto-Sivashinsky systems.
Reference / Citation
View Original"The paper introduces a general, model-agnostic training and inference framework for joint generative forecasting and shows how it enables assessment of forecast robustness and reliability using three complementary uncertainty quantification metrics."