Estimating measurement noise in a time series by exploiting nonstationarity

DOI10.1016/J.CHAOS.2004.02.061zbMath1061.37062OpenAlexW2057929722WikidataQ60145953 ScholiaQ60145953MaRDI QIDQ1766593

Xianqiang Yang

Publication date: 8 March 2005

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2004.02.061

zbMATH Keywords

noisetime seriesnonstationarityEEG signalschaotic Lorenz attractorscomputer-simulated signalsMackey-Glass equations

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) White noise theory (60H40) Applications of dynamical systems (37N99) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10)

Related Items (6)

ANAPT: additive noise analysis for persistence thresholding ⋮ A linearization based non-iterative approach to measure the Gaussian noise level for chaotic time series ⋮ Entropy measures for biological signal analyses ⋮ Error covariance matrix estimation of noisy and dynamically coupled time series ⋮ Nonlinear dynamics of regenerative cutting processes. Comparison of two models ⋮ Complexity testing techniques for time series data: a comprehensive literature review

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• Detecting dynamical nonstationarity in time series data
• Detecting noise in a time series

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