A note on estimating drift and diffusion parameters from time series

DOI10.1016/S0375-9601(02)01474-3zbMath1001.82096WikidataQ58319434 ScholiaQ58319434MaRDI QIDQ1850415

Joseph Barsugli, Philip Sura

Publication date: 3 December 2002

Published in: Physics Letters. A (Search for Journal in Brave)

zbMATH Keywords

errorIto-Taylor expansion

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Transport processes in time-dependent statistical mechanics (82C70)

Related Items (17)

Non-parametric estimation of stochastic differential equations from stationary time-series ⋮ A robust nonparametric framework for reconstruction of stochastic differential equation models ⋮ Forecasting with the Fokker-Planck model: Bayesian setting of parameter ⋮ Nonparametric inference for diffusion processes in systems with smooth evolution ⋮ Stochastic time series with strong, correlated measurement noise: Markov analysis in \(N\) dimensions ⋮ Reconstruction of the modified discrete Langevin equation from persistent time series ⋮ Parametric estimation from approximate data: non-Gaussian diffusions ⋮ An iterative procedure for the estimation of drift and diffusion coefficients of langevin processes ⋮ Kernel-based regression of drift and diffusion coefficients of stochastic processes ⋮ Finite sampling interval effects in Kramers-Moyal analysis ⋮ Bi-SOC-states in one-dimensional random cellular automaton ⋮ Empirical evaluated SDE modelling for dimensionality-reduced systems and its predictability estimates ⋮ A data-analysis method for identifying differential effects of time-delayed feedback forces and periodic driving forces in stochastic systems ⋮ A closed solution to the Fokker-Planck equation applied to forecasting ⋮ Forecasting by splitting a time series using singular value decomposition then using both ARMA and A Fokker Planck equation ⋮ Enhancing the accuracy of a data-driven reconstruction of bivariate jump-diffusion models with corrections for higher orders of the sampling interval ⋮ Discrete Langevin-type equation for p-order persistent time series and procedure of its reconstruction

Cites Work

• Noise-induced transitions. Theory and applications in physics, chemistry, and biology
• Analysis of data sets of stochastic systems
• Handbook of stochastic methods for physics, chemistry and natural sciences.
• Unnamed Item
• Unnamed Item

This page was built for publication: A note on estimating drift and diffusion parameters from time series