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Analysis of stochastic time series in N dimensions in the presence of strong measurement noise

2012/03/11 by Bernd Lehle, B. Lehle, Lehle, B.
Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Complex Systems and Time Series Analysis #Data Analysis #FOS: Physical sciences #Markov Chains and Monte Carlo Methods #Statistics and Probability (physics.data-an) #physics.data-an

paper · pdf · doi:10.48550/arxiv.1203.2334

14 pages, 10 figures

openalex publication_date 2012/03/11 · arxiv created 2012/10/21 · arxiv updated 2012/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

An extension and generalization of a recently presented approach for the analysis of Langevin-type stochastic processes in the presence of strong measurement noise is presented. For a stochastic process in N dimensions which is superimposed with strong, exponentially correlated, Gaussian distributed, measurement noise it is possible to extract the strength and the correlation functions of the noise as well as polynomial approximations of the drift and diffusion functions of the underlying process.

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