Forecasting the underlying potential governing the time series of a dynamical system
Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved. We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation...
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ftunivexeter:oai:ore.exeter.ac.uk:10871/15093 2023-05-15T15:04:30+02:00 Forecasting the underlying potential governing the time series of a dynamical system Livina, VN Lohmann, G Mudelsee, M Lenton, Timothy M. 2013 http://hdl.handle.net/10871/15093 https://doi.org/10.1016/j.physa.2013.04.036 en eng Elsevier http://www.sciencedirect.com/science/article/pii/S037843711300349X Vol. 392, Issue 18, pp. 3891 - 3902 doi:10.1016/j.physa.2013.04.036 NE/F005474/1 289447 http://hdl.handle.net/10871/15093 0378-4371 Physica A: Statistical Mechanics and its Applications This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. CC-BY-NC-ND Potential forecasting Potential analysis Time series analysis Article 2013 ftunivexeter https://doi.org/10.1016/j.physa.2013.04.036 2022-11-20T21:30:48Z Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved. We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation of the empirical probability distribution and extrapolate them in order to forecast the future probability distribution of data. The method is tested on artificial data, used for hindcasting observed climate data, and then applied to forecast Arctic sea-ice time series. The proposed methodology completes a framework for ‘potential analysis’ of tipping points which altogether serves anticipating, detecting and forecasting nonlinear changes including bifurcations using several independent techniques of time series analysis. Although being applied to climatological series in the present paper, the method is very general and can be used to forecast dynamics in time series of any origin. NERC AXA Research Fund European Commission Article in Journal/Newspaper Arctic Sea ice University of Exeter: Open Research Exeter (ORE) Arctic Physica A: Statistical Mechanics and its Applications 392 18 3891 3902 |
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Open Polar |
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University of Exeter: Open Research Exeter (ORE) |
op_collection_id |
ftunivexeter |
language |
English |
topic |
Potential forecasting Potential analysis Time series analysis |
spellingShingle |
Potential forecasting Potential analysis Time series analysis Livina, VN Lohmann, G Mudelsee, M Lenton, Timothy M. Forecasting the underlying potential governing the time series of a dynamical system |
topic_facet |
Potential forecasting Potential analysis Time series analysis |
description |
Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved. We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation of the empirical probability distribution and extrapolate them in order to forecast the future probability distribution of data. The method is tested on artificial data, used for hindcasting observed climate data, and then applied to forecast Arctic sea-ice time series. The proposed methodology completes a framework for ‘potential analysis’ of tipping points which altogether serves anticipating, detecting and forecasting nonlinear changes including bifurcations using several independent techniques of time series analysis. Although being applied to climatological series in the present paper, the method is very general and can be used to forecast dynamics in time series of any origin. NERC AXA Research Fund European Commission |
format |
Article in Journal/Newspaper |
author |
Livina, VN Lohmann, G Mudelsee, M Lenton, Timothy M. |
author_facet |
Livina, VN Lohmann, G Mudelsee, M Lenton, Timothy M. |
author_sort |
Livina, VN |
title |
Forecasting the underlying potential governing the time series of a dynamical system |
title_short |
Forecasting the underlying potential governing the time series of a dynamical system |
title_full |
Forecasting the underlying potential governing the time series of a dynamical system |
title_fullStr |
Forecasting the underlying potential governing the time series of a dynamical system |
title_full_unstemmed |
Forecasting the underlying potential governing the time series of a dynamical system |
title_sort |
forecasting the underlying potential governing the time series of a dynamical system |
publisher |
Elsevier |
publishDate |
2013 |
url |
http://hdl.handle.net/10871/15093 https://doi.org/10.1016/j.physa.2013.04.036 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
http://www.sciencedirect.com/science/article/pii/S037843711300349X Vol. 392, Issue 18, pp. 3891 - 3902 doi:10.1016/j.physa.2013.04.036 NE/F005474/1 289447 http://hdl.handle.net/10871/15093 0378-4371 Physica A: Statistical Mechanics and its Applications |
op_rights |
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.1016/j.physa.2013.04.036 |
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Physica A: Statistical Mechanics and its Applications |
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392 |
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18 |
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