A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation

Abstract Stochastic differential equations (SDEs) are ubiquitous across disciplines, and uncovering SDEs driving observed time series data is a key scientific challenge. Most previous work on this topic has relied on restrictive assumptions, undermining the generality of these approaches. We present...

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Autores principales:	Roman Olson, Soon-Il An, Soong-Ki Kim, Yanan Fan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4e434ec3fd7e4d2b83059614b8431844
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id	oai:doaj.org-article:4e434ec3fd7e4d2b83059614b8431844
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spelling	oai:doaj.org-article:4e434ec3fd7e4d2b83059614b84318442021-12-02T14:16:57ZA novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation10.1038/s41598-021-81162-22045-2322https://doaj.org/article/4e434ec3fd7e4d2b83059614b84318442021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81162-2https://doaj.org/toc/2045-2322Abstract Stochastic differential equations (SDEs) are ubiquitous across disciplines, and uncovering SDEs driving observed time series data is a key scientific challenge. Most previous work on this topic has relied on restrictive assumptions, undermining the generality of these approaches. We present a novel technique to uncover driving probabilistic models that is based on kernel density estimation. The approach relies on few assumptions, does not restrict underlying functional forms, and can be used even on non-Markov systems. When applied to El Niño–Southern Oscillation (ENSO), the fitted empirical model simulations can almost perfectly capture key time series properties of ENSO. This confirms that ENSO could be represented as a two-variable stochastic dynamical system. Our experiments provide insights into ENSO dynamics and suggest that state-dependent noise does not play a major role in ENSO skewness. Our method is general and can be used across disciplines for inverse and forward modeling, to shed light on structure of system dynamics and noise, to evaluate system predictability, and to generate synthetic datasets with realistic properties.Roman OlsonSoon-Il AnSoong-Ki KimYanan FanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Roman Olson Soon-Il An Soong-Ki Kim Yanan Fan A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation
description	Abstract Stochastic differential equations (SDEs) are ubiquitous across disciplines, and uncovering SDEs driving observed time series data is a key scientific challenge. Most previous work on this topic has relied on restrictive assumptions, undermining the generality of these approaches. We present a novel technique to uncover driving probabilistic models that is based on kernel density estimation. The approach relies on few assumptions, does not restrict underlying functional forms, and can be used even on non-Markov systems. When applied to El Niño–Southern Oscillation (ENSO), the fitted empirical model simulations can almost perfectly capture key time series properties of ENSO. This confirms that ENSO could be represented as a two-variable stochastic dynamical system. Our experiments provide insights into ENSO dynamics and suggest that state-dependent noise does not play a major role in ENSO skewness. Our method is general and can be used across disciplines for inverse and forward modeling, to shed light on structure of system dynamics and noise, to evaluate system predictability, and to generate synthetic datasets with realistic properties.
format	article
author	Roman Olson Soon-Il An Soong-Ki Kim Yanan Fan
author_facet	Roman Olson Soon-Il An Soong-Ki Kim Yanan Fan
author_sort	Roman Olson
title	A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation
title_short	A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation
title_full	A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation
title_fullStr	A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation
title_full_unstemmed	A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation
title_sort	novel approach for discovering stochastic models behind data applied to el niño–southern oscillation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4e434ec3fd7e4d2b83059614b8431844
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A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation

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