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.

翻译：随机预测对于能源市场和金融等不确定系统中的高效决策至关重要，其中估计未来情景的完整分布是必不可少的。我们提出了扩散场景树（DST），这是一个利用基于扩散的概率预测模型为多元预测任务构建场景树的通用框架。DST递归地采样未来轨迹，并通过聚类将其组织成树状结构，确保每个阶段满足非预见性约束（决策仅依赖于已观测历史）。我们在纽约州日前电力市场的能量套利优化任务上评估了该框架。实验结果表明，我们的方法在使用相同优化算法时，持续优于采用传统模型生成的场景树以及无模型强化学习基线。此外，将DST用于随机优化能产生更高效的决策策略，相比使用相同基于扩散预测器的确定性和随机模型预测控制变体，通过更好地处理不确定性实现了更高的性能。