Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks

Efforts across diverse domains like economics, energy, and agronomy have focused on developing predictive models for time series data. A spectrum of techniques, spanning from elementary linear models to intricate neural networks and machine learning algorithms, has been explored to achieve accurate...

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Detalhes bibliográficos
Autores: Borrero Sánchez, Juan Diego, Mariscal, Jesús
Tipo de documento: artigo
Data de publicação:2023
País:España
Recursos:Universidad de Huelva (UHU)
Repositório:Arias Montano. Repositorio Institucional de la Universidad de Huelva
Idioma:inglês
OAI Identifier:oai:ariasmontano.uhu.es:10272/22453
Acesso em linha:https://hdl.handle.net/10272/22453
Access Level:Acceso aberto
Palavra-chave:Neural network
Time series prediction models
NAR
Support vector regression
Hybrid forecasting methods
53 Ciencias Económicas
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spelling Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural NetworksBorrero Sánchez, Juan DiegoMariscal, JesúsNeural networkTime series prediction modelsNARSupport vector regressionHybrid forecasting methods53 Ciencias EconómicasEfforts across diverse domains like economics, energy, and agronomy have focused on developing predictive models for time series data. A spectrum of techniques, spanning from elementary linear models to intricate neural networks and machine learning algorithms, has been explored to achieve accurate forecasts. The hybrid ARIMA-SVR model has garnered attention due to its fusion of a foundational linear model with error correction capabilities. However, its use is limited to stationary time series data, posing a significant challenge. To overcome these limitations and drive progress, we propose the innovative NAR–SVR hybrid method. Unlike its predecessor, this approach breaks free from stationarity and linearity constraints, leading to improved model performance solely through historical data exploitation. This advancement significantly reduces the time and computational resources needed for precise predictions, a critical factor in univariate economic time series forecasting. We apply the NAR–SVR hybrid model in three scenarios: Spanish berry daily yield data from 2018 to 2021, daily COVID-19 cases in three countries during 2020, and the daily Bitcoin price time series from 2015 to 2020. Through extensive comparative analyses with other time series prediction models, our results substantiate that our novel approach consistently outperforms its counterparts. By transcending stationarity and linearity limitations, our hybrid methodology establishes a new paradigm for univariate time series forecasting, revolutionizing the field and enhancing predictive capabilities across various domains as highlighted in this study.MDPI20232023-09-0120232023-09-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10272/22453reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelvainstname:Universidad de Huelva (UHU)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ariasmontano.uhu.es:10272/224532026-06-02T14:58:11Z
dc.title.none.fl_str_mv Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
title Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
spellingShingle Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
Borrero Sánchez, Juan Diego
Neural network
Time series prediction models
NAR
Support vector regression
Hybrid forecasting methods
53 Ciencias Económicas
title_short Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
title_full Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
title_fullStr Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
title_full_unstemmed Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
title_sort Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
dc.creator.none.fl_str_mv Borrero Sánchez, Juan Diego
Mariscal, Jesús
author Borrero Sánchez, Juan Diego
author_facet Borrero Sánchez, Juan Diego
Mariscal, Jesús
author_role author
author2 Mariscal, Jesús
author2_role author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Neural network
Time series prediction models
NAR
Support vector regression
Hybrid forecasting methods
53 Ciencias Económicas
topic Neural network
Time series prediction models
NAR
Support vector regression
Hybrid forecasting methods
53 Ciencias Económicas
description Efforts across diverse domains like economics, energy, and agronomy have focused on developing predictive models for time series data. A spectrum of techniques, spanning from elementary linear models to intricate neural networks and machine learning algorithms, has been explored to achieve accurate forecasts. The hybrid ARIMA-SVR model has garnered attention due to its fusion of a foundational linear model with error correction capabilities. However, its use is limited to stationary time series data, posing a significant challenge. To overcome these limitations and drive progress, we propose the innovative NAR–SVR hybrid method. Unlike its predecessor, this approach breaks free from stationarity and linearity constraints, leading to improved model performance solely through historical data exploitation. This advancement significantly reduces the time and computational resources needed for precise predictions, a critical factor in univariate economic time series forecasting. We apply the NAR–SVR hybrid model in three scenarios: Spanish berry daily yield data from 2018 to 2021, daily COVID-19 cases in three countries during 2020, and the daily Bitcoin price time series from 2015 to 2020. Through extensive comparative analyses with other time series prediction models, our results substantiate that our novel approach consistently outperforms its counterparts. By transcending stationarity and linearity limitations, our hybrid methodology establishes a new paradigm for univariate time series forecasting, revolutionizing the field and enhancing predictive capabilities across various domains as highlighted in this study.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-09-01
2023
2023-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10272/22453
url https://hdl.handle.net/10272/22453
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelva
instname:Universidad de Huelva (UHU)
instname_str Universidad de Huelva (UHU)
reponame_str Arias Montano. Repositorio Institucional de la Universidad de Huelva
collection Arias Montano. Repositorio Institucional de la Universidad de Huelva
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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