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This paper proposes a residual-anchor Neural SDE that separates a learned conditional baseline from a stochastic residual, aiming to stabilize conditional time-series generation when variables have strong level dependence or limited data. The evaluation is on financial series, including conditional interest-rate scenarios, with a comparison against a time-series foundation model. The claims of plausible paths, competitive point accuracy, and calibrated uncertainty are promising, but they rest on a limited set of benchmarks, so readers should check the experimental details before generalizing.
Conditional Neural SDEs for Financial Time-Series Generation
arXiv:2610.41405v1 Announce Type: cross Abstract: Conditional generation of time-series paths is a central problem in probabilistic modeling, where the goal is to simulate future trajectories that are both realistic and responsive to external context. It has direct applications in finance and economics. Neural stochastic differential equations provide a natural framework for this task by combining continuous-time stochastic dynamics with learnable drift and diffusion functions. However, directly modeling the target state can be unstable in conditional settings, especially when the target and conditioning variables have strong level dependence, limited training data, or different dimensions.We propose a residualanchor Neural SDE architecture that decomposes the target into a learned conditional anchor and a stochastic residual. The anchor captures the context-dependent baseline, while the Neural SDE (Stochastic Differential Equation) models the residual dynamics around this baseline. This separation reduces the burden on the stochastic dynamics and generalizes spread-based representations to more general conditioning settings.We evaluate the approach on real-world financial time series, including conditional interest-rate scenarios and a comparison with a time-series foundation model. The results show that the proposed model can generate plausible conditional paths, maintain competitive point accuracy, and provide calibrated uncertainty estimates.
Abstract:Conditional generation of time-series paths is a central problem in probabilistic modeling, where the goal is to simulate future trajectories that are both realistic and responsive to external context. It has direct applications in finance and economics. Neural stochastic differential equations provide a natural framework for this task by combining continuous-time stochastic dynamics with learnable drift and diffusion functions. However, directly modeling the target state can be unstable in conditional settings, especially when the target and conditioning variables have strong level dependence, limited training data, or different this http URL propose a residualanchor Neural SDE architecture that decomposes the target into a learned conditional anchor and a stochastic residual. The anchor captures the context-dependent baseline, while the Neural SDE (Stochastic Differential Equation) models the residual dynamics around this baseline. This separation reduces the burden on the stochastic dynamics and generalizes spread-based representations to more general conditioning this http URL evaluate the approach on real-world financial time series, including conditional interest-rate scenarios and a comparison with a time-series foundation model. The results show that the proposed model can generate plausible conditional paths, maintain competitive point accuracy, and provide calibrated uncertainty estimates.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2610.41405 [stat.ML]
(or arXiv:2610.41405v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.41405 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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