Inference for stochastic differential equations driven by weighted sub-fractional Brownian motion using neural networks and the Euler approximation

arXiv:2610.00793 · stat.ML, cs.LG · Submitted 2026-09-30 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/joseramirezgonzalez/NN_wsBm

Terminology

• Adam — 65× in this paper · explained in 2 episodes
• Adaptation — 12× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Temporal Correlations — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Adam: A Method for Stochastic Optimization
• Weighted Sub-fractional Brownian Motion Process: Properties and Generalizations
• A new family of Gaussian processes for modeling animal movement: application to bat telemetry data

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