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The model improves accuracy using only historical data. Results on Spanish berry yields, COVID-19 cases, and Bitcoin prices show consistent superiority over competing approaches.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/elevating-univariate-time-series-forecasting-innovative-svr-empowered-nonlinear-autoregressive-neural-networks/290960/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/elevating-univariate-time-series-forecasting-innovative-svr-empowered-nonlinear-autoregressive-neural-networks/290960.png","ImageObject",300,407,{"name":42,"@type":43},"Quinn","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-19","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What limitation affects ARIMA-SVR time series forecasting?","Question",{"text":63,"@type":64},"ARIMA-SVR is mainly limited to stationary time series, which restricts its applicability.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What does the proposed NAR–SVR hybrid method change?",{"text":68,"@type":64},"It breaks free from stationarity and linearity constraints while relying on historical data to improve forecasting performance.",{"name":70,"@type":61,"acceptedAnswer":71},"Which datasets are used to evaluate the NAR–SVR hybrid model?",{"text":72,"@type":64},"The study evaluates the method on Spanish berry daily yields (2018–2021), daily COVID-19 cases in three countries (2020), and daily Bitcoin prices (2015–2020).","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290960,1789643044,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","algorithms  \nArticle  \nElevating Univariate Time Series Forecasting: Innovative  \nSVR-Empowered Nonlinear Autoregressive Neural Networks  \nJuan D. Borrero 1,􀀃,† and Jesus Mariscal 2,†  \nCitation: Borrero, J.D.; Mariscal, J. Elevating Univariate Time Series Forecasting: Innovative  \nSVR-Empowered Nonlinear Autoregressive Neural Networks. Algorithms 2023, 16, 423. [https://](https://)[ ](https://)[doi.org/10.3390/a16090423](doi.org/10.3390/a16090423)  \nAcademic Editor: Frank Werner  \nReceived: 14 July 2023  \nRevised: 14 August 2023  \nAccepted: 28 August 2023  \nPublished: 2 September 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Management and Marketing, University of Huelva, Pza. de la Merced s/n,  \n21002 Huelva, Spain  \n2 Agricultural Economics Research Group, Department of Management and Marketing, University of Huelva, Pza. de la Merced s/n, 21002 Huelva, Spain; jesus.mariscal@dem.uhu.es  \n* Correspondence: jdiego@uhu.es  \n† These authors contributed equally to this work.  \nAbstract: 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.  \nKeywords: neural network; time series prediction models; NAR; support vector regression; hybrid forecasting methods  \n1. Introduction  \nModeling and forecasting of time series data play a pivotal role in various industries and organizations, spanning economic, social, and environmental domains. These practices enable accurate predictions, facilitating effective prevention, control, and planning within a given area. Consequently, the development of intelligent predictive systems for time series data has garnered significant attention from the scientific community, resulting in a wealth of research publications.  \nHowever, the challenge of achieving precise predictions and the necessity to enhance existing algorithms have made predictive models an enduring subject of research. Additionally, since each time series possesses unique characteristics, no single optimal technique exists for predicting future values. Instead, the nature of the observed variable dictates the most suitable model for a specific case.  \nTh","cbCailBvtdPKusrF","https://ap.wps.com/l/cbCailBvtdPKusrF","pdf",3758919,15,"English","# Introduction\n# Modeling Approaches\n## Linear models (ARIMA family)\n## Nonlinear and hybrid models (SVR, NAR, NAR–SVR)\n# Proposed Method and Evaluation","[{\"question\":\"What limitation affects ARIMA-SVR time series forecasting?\",\"answer\":\"ARIMA-SVR is mainly limited to stationary time series, which restricts its applicability.\"},{\"question\":\"What does the proposed NAR–SVR hybrid method change?\",\"answer\":\"It breaks free from stationarity and linearity constraints while relying on historical data to improve forecasting performance.\"},{\"question\":\"Which datasets are used to evaluate the NAR–SVR hybrid model?\",\"answer\":\"The study evaluates the method on Spanish berry daily yields (2018–2021), daily COVID-19 cases in three countries (2020), and daily Bitcoin prices (2015–2020).\"}]","Elevating Univariate Time Series Forecasting - Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks | PDF",38]