用扩散模型生成场景树，提升多阶段决策中对不确定性的处理能力。
Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization
- 基于扩散模型递归采样轨迹，聚类构建满足非前瞻性的场景树。
- 在纽约电力市场能源套利任务中，决策效果优于传统方法与强化学习基线。
- 适合需要精准建模不确定性演化的能源、金融等动态系统优化场景。
随机预测对能源市场和金融等不确定系统中的高效决策至关重要，需估计未来情景的完整分布。本文提出扩散场景树（DST），一种利用基于扩散的概率预测模型构建场景树的通用框架，为控制任务提供系统演化的结构化表示。DST通过递归采样未来轨迹并聚类形成树状结构，确保每阶段决策仅依赖于历史观测（非前瞻性），相比仅用于预测的模型，能更优地表征不确定性。我们将DST集成至模型预测控制（MPC）中，并在纽约州日前电力市场的能源套利任务上进行评估。实验结果表明，该方法显著优于使用传统模型生成场景树的相同优化算法。此外，使用DST进行随机优化可获得更高效的决策策略，相较于采用相同扩散预测器的确定性与随机型MPC及简单无模型强化学习基线，展现出更强的不确定性处理能力。
原文摘要 · Abstract (English)
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 using diffusion-based probabilistic forecasting models to provide a structured model of system evolution for control tasks. 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, offering a superior representation of uncertainty compared to using predictive models solely for forecasting system evolution. We integrate DST into Model Predictive Control (MPC) and evaluate it on energy arbitrage in New York State's day-ahead electricity market. Experimental results show that our approach significantly outperforms the same optimization algorithms that use scenario trees generated by more conventional models. Furthermore, using DST for stochastic optimization yields more efficient decision policies by better handling uncertainty than deterministic and stochastic MPC variants using the same diffusion-based forecaster, and simple Model-Free Reinforcement Learning (RL) baselines.
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