Diffusion Models for Adaptive Sequential Data Generation
Generating realistic synthetic sequential data is critical in real-world applications across operations research, finance, healthcare, energy systems, and scientific computing, where time-indexed observations are used for prediction, simulation, risk assessment, and data-driven decision-making. While diffusion models have achieved remarkable success in generating static data, their direct extensions to sequential settings often fail to capture temporal dependence and information structure. Designing diffusion models that can simulate sequential data in an adapted manner, and hence without anticipation of future information, therefore remains an open challenge. In this work, we propose a s...
| Authors | Haoyang Cao, Minshuo Chen, Yinbin Han et al. |
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| Primary category | cs.LG |
| Published | 4 June 2026 |
| Last updated at source | 14 June 2026 |
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