arXiv:2606.01634v1 Announce Type: new Abstract: Generating realistic time series is essential for scientific research and real-world applications. However, existing methods often emphasize overall distributional fidelity while failing to faithfully capture extreme events. To advance existing research, we propose E4GEN, an explainable diffusion framework for extreme event-aware time-series generation. E4GEN provides systematic insights into when, what, and how to control extreme-event generation through three key components. First, E-Activator learns the dataset-adaptive extreme-control signal
The increasing sophistication of generative AI models demands higher fidelity, especially for critical applications where extreme events (e.g., financial crashes, extreme weather) are disproportionately impactful yet hard to model accurately.
Improved generation of realistic time series, particularly extreme events, will significantly enhance predictive modeling, risk assessment, and decision-making in high-stakes environments.
Current time-series generation methods often fail to capture extreme events; E4GEN introduces an explainable framework to control and realistically generate these critical outliers, changing how simulations and synthetic data are produced.
- · Financial modeling firms
- · Climate scientists
- · Healthcare research
- · Generative AI developers
- · Traditional time-series modeling approaches
- · Organizations relying on simplistic data augmentation
More accurate crisis forecasting and risk management will be possible across various sectors.
The ability to generate realistic extreme events could accelerate research into their causes and mitigation strategies.
Synthetic datasets with faithful extreme event representation might reduce the need for sensitive real-world data in some applications, raising new privacy and data availability dynamics.
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Read at arXiv cs.LG