Quantifying uncertainty with a derivative tracking SDE model and application to wind power forecast data

DOI10.1007/s11222-021-10040-8zbMath1475.62021arXiv2006.15907OpenAlexW3198141577MaRDI QIDQ2058882

Raúl Tempone, Ahmed Kebaier, Marco Scavino, Renzo Caballero

Publication date: 10 December 2021

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.15907

zbMATH Keywords

model selectionuncertainty quantificationwind powerforecasting errorfixed-point likelihood numerical optimizationLamperti spacetime-inhomogeneous Jacobi diffusion

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Numerical optimization and variational techniques (65K10)

Cites Work

• Unnamed Item
• High-order approximation of Pearson diffusion processes
• Approximation of transition densities of stochastic differential equations by saddlepoint methods applied to small-time Ito-Taylor sample-path expansions
• Maximum likelihood estimation of time-inhomogeneous diffusions.
• A simple construction of certain diffusion processes
• Affine diffusions and related processes: simulation, theory and applications
• Simulation and inference for stochastic differential equations. With R examples.
• Estimation for nonlinear stochastic differential equations by a local linearization method¹
• The Jacobi diffusion process as a neuronal model
• Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models
• Applied Stochastic Differential Equations
• Stochastic Differential Equations
• The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes
• Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach