Extracting strong measurement noise from stochastic series: applications to empirical data
It is a big challenge in the analysis of experimental data to disentangle the unavoidable measurement noise from the intrinsic dynamical noise. Here we present a general operational method to extract measurement noise from stochastic time series, even in the case when the amplitudes of measurement n...
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ftdatacite:10.48550/arxiv.0912.2844 2023-05-15T17:33:13+02:00 Extracting strong measurement noise from stochastic series: applications to empirical data Lind, Pedro G. Haase, Maria Böttcher, Frank Peinke, Joachim Kleinhans, David Friedrich, Rudolf 2009 https://dx.doi.org/10.48550/arxiv.0912.2844 https://arxiv.org/abs/0912.2844 unknown arXiv https://dx.doi.org/10.1103/physreve.81.041125 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Chaotic Dynamics nlin.CD FOS Physical sciences article-journal Article ScholarlyArticle Text 2009 ftdatacite https://doi.org/10.48550/arxiv.0912.2844 https://doi.org/10.1103/physreve.81.041125 2022-04-01T14:59:25Z It is a big challenge in the analysis of experimental data to disentangle the unavoidable measurement noise from the intrinsic dynamical noise. Here we present a general operational method to extract measurement noise from stochastic time series, even in the case when the amplitudes of measurement noise and uncontaminated signal are of the same order of magnitude. Our approach is based on a recently developed method for a nonparametric reconstruction of Langevin processes. Minimizing a proper non-negative function the procedure is able to correctly extract strong measurement noise and to estimate drift and diffusion coefficients in the Langevin equation describing the evolution of the original uncorrupted signal. As input, the algorithm uses only the two first conditional moments extracted directly from the stochastic series and is therefore suitable for a broad panoply of different signals. To demonstrate the power of the method we apply the algorithm to synthetic as well as climatological measurement data, namely the daily North Atlantic Oscillation index, shedding new light on the discussion of the nature of its underlying physical processes. : 15 pages, 9 figures Text North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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language |
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topic |
Chaotic Dynamics nlin.CD FOS Physical sciences |
spellingShingle |
Chaotic Dynamics nlin.CD FOS Physical sciences Lind, Pedro G. Haase, Maria Böttcher, Frank Peinke, Joachim Kleinhans, David Friedrich, Rudolf Extracting strong measurement noise from stochastic series: applications to empirical data |
topic_facet |
Chaotic Dynamics nlin.CD FOS Physical sciences |
description |
It is a big challenge in the analysis of experimental data to disentangle the unavoidable measurement noise from the intrinsic dynamical noise. Here we present a general operational method to extract measurement noise from stochastic time series, even in the case when the amplitudes of measurement noise and uncontaminated signal are of the same order of magnitude. Our approach is based on a recently developed method for a nonparametric reconstruction of Langevin processes. Minimizing a proper non-negative function the procedure is able to correctly extract strong measurement noise and to estimate drift and diffusion coefficients in the Langevin equation describing the evolution of the original uncorrupted signal. As input, the algorithm uses only the two first conditional moments extracted directly from the stochastic series and is therefore suitable for a broad panoply of different signals. To demonstrate the power of the method we apply the algorithm to synthetic as well as climatological measurement data, namely the daily North Atlantic Oscillation index, shedding new light on the discussion of the nature of its underlying physical processes. : 15 pages, 9 figures |
format |
Text |
author |
Lind, Pedro G. Haase, Maria Böttcher, Frank Peinke, Joachim Kleinhans, David Friedrich, Rudolf |
author_facet |
Lind, Pedro G. Haase, Maria Böttcher, Frank Peinke, Joachim Kleinhans, David Friedrich, Rudolf |
author_sort |
Lind, Pedro G. |
title |
Extracting strong measurement noise from stochastic series: applications to empirical data |
title_short |
Extracting strong measurement noise from stochastic series: applications to empirical data |
title_full |
Extracting strong measurement noise from stochastic series: applications to empirical data |
title_fullStr |
Extracting strong measurement noise from stochastic series: applications to empirical data |
title_full_unstemmed |
Extracting strong measurement noise from stochastic series: applications to empirical data |
title_sort |
extracting strong measurement noise from stochastic series: applications to empirical data |
publisher |
arXiv |
publishDate |
2009 |
url |
https://dx.doi.org/10.48550/arxiv.0912.2844 https://arxiv.org/abs/0912.2844 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
https://dx.doi.org/10.1103/physreve.81.041125 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.0912.2844 https://doi.org/10.1103/physreve.81.041125 |
_version_ |
1766131650373091328 |