A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data

In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<mi<L</mi<<mi<R...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gholamreza Hesamian [verfasserIn] Arne Johannssen [verfasserIn] Nataliya Chukhrova [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	fuzzy regression fuzzy time series model nonparametric time series analysis time series analysis

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 11(2023), 13, p 2800
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:13, p 2800

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math11132800

Katalog-ID:	DOAJ093997760

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spelling	10.3390/math11132800 doi (DE-627)DOAJ093997760 (DE-599)DOAJ40b25d89f93544cc86f46b772127a3eb DE-627 ger DE-627 rakwb eng QA1-939 Gholamreza Hesamian verfasserin aut A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<mi<L</mi<<mi<R</mi<</mrow<</semantics<</math<</inline-formula< fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications. fuzzy regression fuzzy time series model nonparametric time series analysis time series analysis Mathematics Arne Johannssen verfasserin aut Nataliya Chukhrova verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 13, p 2800 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:13, p 2800 https://doi.org/10.3390/math11132800 kostenfrei https://doaj.org/article/40b25d89f93544cc86f46b772127a3eb kostenfrei https://www.mdpi.com/2227-7390/11/13/2800 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 13, p 2800
allfields_unstemmed	10.3390/math11132800 doi (DE-627)DOAJ093997760 (DE-599)DOAJ40b25d89f93544cc86f46b772127a3eb DE-627 ger DE-627 rakwb eng QA1-939 Gholamreza Hesamian verfasserin aut A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<mi<L</mi<<mi<R</mi<</mrow<</semantics<</math<</inline-formula< fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications. fuzzy regression fuzzy time series model nonparametric time series analysis time series analysis Mathematics Arne Johannssen verfasserin aut Nataliya Chukhrova verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 13, p 2800 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:13, p 2800 https://doi.org/10.3390/math11132800 kostenfrei https://doaj.org/article/40b25d89f93544cc86f46b772127a3eb kostenfrei https://www.mdpi.com/2227-7390/11/13/2800 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 13, p 2800
allfieldsGer	10.3390/math11132800 doi (DE-627)DOAJ093997760 (DE-599)DOAJ40b25d89f93544cc86f46b772127a3eb DE-627 ger DE-627 rakwb eng QA1-939 Gholamreza Hesamian verfasserin aut A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<mi<L</mi<<mi<R</mi<</mrow<</semantics<</math<</inline-formula< fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications. fuzzy regression fuzzy time series model nonparametric time series analysis time series analysis Mathematics Arne Johannssen verfasserin aut Nataliya Chukhrova verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 13, p 2800 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:13, p 2800 https://doi.org/10.3390/math11132800 kostenfrei https://doaj.org/article/40b25d89f93544cc86f46b772127a3eb kostenfrei https://www.mdpi.com/2227-7390/11/13/2800 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 13, p 2800
allfieldsSound	10.3390/math11132800 doi (DE-627)DOAJ093997760 (DE-599)DOAJ40b25d89f93544cc86f46b772127a3eb DE-627 ger DE-627 rakwb eng QA1-939 Gholamreza Hesamian verfasserin aut A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<mi<L</mi<<mi<R</mi<</mrow<</semantics<</math<</inline-formula< fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications. fuzzy regression fuzzy time series model nonparametric time series analysis time series analysis Mathematics Arne Johannssen verfasserin aut Nataliya Chukhrova verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 13, p 2800 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:13, p 2800 https://doi.org/10.3390/math11132800 kostenfrei https://doaj.org/article/40b25d89f93544cc86f46b772127a3eb kostenfrei https://www.mdpi.com/2227-7390/11/13/2800 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 13, p 2800
language	English
source	In Mathematics 11(2023), 13, p 2800 volume:11 year:2023 number:13, p 2800
sourceStr	In Mathematics 11(2023), 13, p 2800 volume:11 year:2023 number:13, p 2800
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	fuzzy regression fuzzy time series model nonparametric time series analysis time series analysis Mathematics
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authorswithroles_txt_mv	Gholamreza Hesamian @@aut@@ Arne Johannssen @@aut@@ Nataliya Chukhrova @@aut@@
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callnumber-first	Q - Science
author	Gholamreza Hesamian
spellingShingle	Gholamreza Hesamian misc QA1-939 misc fuzzy regression misc fuzzy time series model misc nonparametric time series analysis misc time series analysis misc Mathematics A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data
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topic_title	QA1-939 A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data fuzzy regression fuzzy time series model nonparametric time series analysis time series analysis
topic	misc QA1-939 misc fuzzy regression misc fuzzy time series model misc nonparametric time series analysis misc time series analysis misc Mathematics
topic_unstemmed	misc QA1-939 misc fuzzy regression misc fuzzy time series model misc nonparametric time series analysis misc time series analysis misc Mathematics
topic_browse	misc QA1-939 misc fuzzy regression misc fuzzy time series model misc nonparametric time series analysis misc time series analysis misc Mathematics
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title	A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data
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title_full	A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data
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author_browse	Gholamreza Hesamian Arne Johannssen Nataliya Chukhrova
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title_sort	three-stage nonparametric kernel-based time series model based on fuzzy data
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title_auth	A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data
abstract	In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<mi<L</mi<<mi<R</mi<</mrow<</semantics<</math<</inline-formula< fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications.
abstractGer	In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<mi<L</mi<<mi<R</mi<</mrow<</semantics<</math<</inline-formula< fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications.
abstract_unstemmed	In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<mi<L</mi<<mi<R</mi<</mrow<</semantics<</math<</inline-formula< fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications.
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title_short	A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data
url	https://doi.org/10.3390/math11132800 https://doaj.org/article/40b25d89f93544cc86f46b772127a3eb https://www.mdpi.com/2227-7390/11/13/2800 https://doaj.org/toc/2227-7390
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