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[2509.14832] Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization
Summary
The paper presents a Diffusion Scenario Tree (DST) framework for multivariate time series prediction and multistage stochastic optimization, enhancing decision-making in uncertain systems like energy markets.
Why It Matters
This research is significant as it addresses the challenge of uncertainty in decision-making processes, particularly in critical sectors such as energy and finance. By improving scenario tree generation through diffusion-based models, it offers a more effective approach for predictive control and optimization, potentially leading to better resource management and operational efficiency.
Key Takeaways
- The Diffusion Scenario Tree (DST) framework improves scenario tree generation for multivariate time series.
- DST enhances decision-making in uncertain environments by ensuring non-anticipativity.
- Experimental results indicate DST outperforms traditional models in energy market optimization.
- Integrating DST with Model Predictive Control (MPC) leads to more efficient decision policies.
- The approach is beneficial for sectors requiring robust stochastic forecasting.
Computer Science > Machine Learning arXiv:2509.14832 (cs) [Submitted on 18 Sep 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization Authors:Stelios Zarifis, Ioannis Kordonis, Petros Maragos View a PDF of the paper titled Diffusion-Based Scenario Tree Generation for Multivariate Time Series Prediction and Multistage Stochastic Optimization, by Stelios Zarifis and 2 other authors View PDF HTML (experimental) 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 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 optimizat...