Forecasting the underlying potential governing the time series of a dynamical system

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 for...

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Main Authors: Livina, V. N., Lohmann, G., Mudelsee, M., Lenton, T. M.
Format: Text
Language:unknown
Published: arXiv 2012
Subjects:
Data Analysis, Statistics and Probability physics.data-an
Geophysics physics.geo-ph
FOS Physical sciences
Arctic
Sea ice
Online Access:https://dx.doi.org/10.48550/arxiv.1212.4090
https://arxiv.org/abs/1212.4090
id ftdatacite:10.48550/arxiv.1212.4090
record_format openpolar
spelling ftdatacite:10.48550/arxiv.1212.4090 2023-05-15T15:04:25+02:00 Forecasting the underlying potential governing the time series of a dynamical system Livina, V. N. Lohmann, G. Mudelsee, M. Lenton, T. M. 2012 https://dx.doi.org/10.48550/arxiv.1212.4090 https://arxiv.org/abs/1212.4090 unknown arXiv https://dx.doi.org/10.1016/j.physa.2013.04.036 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Data Analysis, Statistics and Probability physics.data-an Geophysics physics.geo-ph FOS Physical sciences article-journal Article ScholarlyArticle Text 2012 ftdatacite https://doi.org/10.48550/arxiv.1212.4090 https://doi.org/10.1016/j.physa.2013.04.036 2022-04-01T13:29:21Z 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 non-linear 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. : 17 pages, 7 figures; the manuscript is accepted in Physica A Text Arctic Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Data Analysis, Statistics and Probability physics.data-an
Geophysics physics.geo-ph
FOS Physical sciences
spellingShingle Data Analysis, Statistics and Probability physics.data-an
Geophysics physics.geo-ph
FOS Physical sciences
Livina, V. N.
Lohmann, G.
Mudelsee, M.
Lenton, T. M.
Forecasting the underlying potential governing the time series of a dynamical system
topic_facet Data Analysis, Statistics and Probability physics.data-an
Geophysics physics.geo-ph
FOS Physical sciences
description 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 non-linear 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. : 17 pages, 7 figures; the manuscript is accepted in Physica A
format Text
author Livina, V. N.
Lohmann, G.
Mudelsee, M.
Lenton, T. M.
author_facet Livina, V. N.
Lohmann, G.
Mudelsee, M.
Lenton, T. M.
author_sort Livina, V. N.
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 arXiv
publishDate 2012
url https://dx.doi.org/10.48550/arxiv.1212.4090
https://arxiv.org/abs/1212.4090
geographic Arctic
geographic_facet Arctic
genre Arctic
Sea ice
genre_facet Arctic
Sea ice
op_relation https://dx.doi.org/10.1016/j.physa.2013.04.036
op_rights arXiv.org perpetual, non-exclusive license
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
op_doi https://doi.org/10.48550/arxiv.1212.4090
https://doi.org/10.1016/j.physa.2013.04.036
_version_ 1766336186687684608

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