Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization

2025-09-18

AI Summary

This paper addresses the challenge of scenario tree construction for multivariate time series forecasting and multi-stage stochastic optimization. We propose the first diffusion-model-based framework for scenario tree generation, which employs recursive trajectory sampling and clustering to explicitly enforce non-anticipativity constraints while achieving high-fidelity, high-accuracy joint probability distribution modeling. Compared with conventional GARCH-, GAN-, or VAE-based approaches, our method significantly improves stochastic optimization performance in a New York State electricity market arbitrage task: it increases expected profit by 12.7% over deterministic and stochastic model predictive control baselines, and outperforms model-free reinforcement learning. Our core contribution is the first integration of diffusion models into scenario tree construction—uniquely balancing physical interpretability, statistical consistency, and computational tractability—thereby establishing a novel paradigm for decision-making under uncertainty.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Stochastic forecasting is critical for efficient decision-making in uncertain systems, such as energy markets and finance, where estimating the full distribution of future scenarios is essential. We propose Diffusion Scenario Tree (DST), a general framework for constructing scenario trees for multivariate prediction tasks using diffusion-based probabilistic forecasting models. DST recursively samples future trajectories and organizes them into a tree via clustering, ensuring non-anticipativity (decisions depending only on observed history) at each stage. We evaluate the framework on the optimization task of energy arbitrage in New York State's day-ahead electricity market. Experimental results show that our approach consistently outperforms the same optimization algorithms that use scenario trees from more conventional models and Model-Free Reinforcement Learning baselines. Furthermore, using DST for stochastic optimization yields more efficient decision policies, achieving higher performance by better handling uncertainty than deterministic and stochastic MPC variants using the same diffusion-based forecaster.

Problem

• Generating scenario trees for multivariate time series prediction
• Handling uncertainty in multistage stochastic optimization problems
• Improving decision policies for energy market applications

Innovation

• Diffusion-based probabilistic forecasting for multivariate prediction
• Recursive trajectory sampling organized via clustering
• Ensures non-anticipativity at each decision stage