arXiv:2605.28507v1 Announce Type: new Abstract: Recent work on the sequence universality of State Space Models (SSMs) has introduced efficient, maximally expressive continuous-time approaches for time-series modelling. While these works focus on discriminative settings, we extend this perspective to generative time-series modelling by proving that maximally expressive Structured Linear Controlled Differential Equations (SLiCEs) are universal time-series generators, in the sense that they can approximate the induced path laws of continuous causal pushforwards on compact latent sets in $W_\infty
This research builds on recent advancements in State Space Models, extending their theoretical capabilities from discriminative to generative tasks for time-series modeling.
The development of universal time-series generators could significantly enhance the ability to create realistic synthetic data for training AI models, simulate complex systems, and predict future states.
This theoretical proof-of-concept could enable more robust and versatile generative AI for sequential data, potentially reducing reliance on extensive real-world datasets in some applications.
- · AI researchers
- · Generative AI companies
- · Data scientists
- · Simulation platforms
- · Companies reliant solely on discriminative time-series models
- · Data collection services where synthetic data can substitute
- · Less sophisticated generative modeling techniques
Improved generative models for financial forecasting, scientific simulation, and synthetic data creation.
Accelerated development of AI systems in fields like healthcare and autonomous driving due to enhanced synthetic data capabilities.
Ethical and safety challenges will arise concerning the creation and use of highly realistic, potentially indistinguishable simulated time-series data.
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Read at arXiv cs.LG