A new recurrent spiking pi sigma artificial neural network for the forecasting problem

A new recurrent spiking pi sigma artificial neural network for the forecasting problem | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A new recurrent spiking pi sigma artificial neural network for the forecasting problem Erol Egrioglu, Eren Bas, Gulsen Albayrak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8592450/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Due to their flexible model structures and their success in nonlinear modelling, artificial neural networks provide good alternatives for solving the forecasting problem. It is seen that different artificial neuron models can positively affect the forecasting performance and pave the way for the creation of new artificial neural network models. In this study, a new artificial neural network with an architecture based on multiplicative and additive neuron models and using the feedback logic in exponential smoothing methods is presented. The training algorithm of the proposed neural network is based on particle swarm optimization using a dynamic fitness function that gives more weight to recent observations. The performance of the proposed new neural network is investigated with the help of statistical hypothesis tests by comparing it with other methods in the literature on Nasdaq stock exchange time series. As a result of the study, it is empirically observed that the proposed neural network has a successful forecasting performance. Physical sciences/Engineering Physical sciences/Mathematics and computing Artificial neural networks Shallow neural networks Recurrent artificial neural networks Forecasting Time series analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviews received at journal 24 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor invited by journal 20 Jan, 2026 Editor assigned by journal 16 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 13 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8592450","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":580374573,"identity":"96f03a65-d22b-43c8-8c9c-5b4df0daf6bc","order_by":0,"name":"Erol Egrioglu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABIUlEQVRIie2RMUvDQBTHrxwky4tdT9LSr3AQqIhiv8qJUJeAHTukIS69RTpXKH6GTs5XDpLlBneXFKEuDv0E6rtgxELS2eF+8B6Pgx/vzztCHI7/SUfZ7lXDlMDvc3bEqRUczIFCjyvV0Jn/Wd6mdEOtMM/s7sSXLzp4mvXOpNyFE3LZX6tuvm9QThdjgXmK8zmYiQ6eC+gZMwyXZBytFaXLBoUb4Jgn5x6LBSo5MBZ7IRB9bZWmYKNK+ayVFSqD9x0qX60KB6tkCSq3SgdZglvIEBXVqjDjCSVyxT0wZLPKFTCIowvgN9GjplHjxR7optwnKR9IuS0/knTE/GL7CtOr/qK4f2u8skUQjR04Nl0Hxmr/SUuK5Zc/g8PhcDgO+QbCfmD5BUoLYwAAAABJRU5ErkJggg==","orcid":"","institution":"Giresun University","correspondingAuthor":true,"prefix":"","firstName":"Erol","middleName":"","lastName":"Egrioglu","suffix":""},{"id":580374574,"identity":"7bd90c34-0909-4c98-b6a6-2104711b2e9c","order_by":1,"name":"Eren Bas","email":"","orcid":"","institution":"Giresun University","correspondingAuthor":false,"prefix":"","firstName":"Eren","middleName":"","lastName":"Bas","suffix":""},{"id":580374575,"identity":"f8fbc2c8-6355-4c1d-b124-be859cac2f00","order_by":2,"name":"Gulsen Albayrak","email":"","orcid":"","institution":"Giresun University","correspondingAuthor":false,"prefix":"","firstName":"Gulsen","middleName":"","lastName":"Albayrak","suffix":""}],"badges":[],"createdAt":"2026-01-13 13:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8592450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8592450/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-49954-6","type":"published","date":"2026-04-21T15:59:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":107928052,"identity":"e9503f6b-43a6-4f24-b539-7ba6e77fdb67","added_by":"auto","created_at":"2026-04-27 16:06:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1053355,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8592450/v1_covered_bcd2e176-6fb8-44fa-80f4-f7f051efa91b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A new recurrent spiking pi sigma artificial neural network for the forecasting problem","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial neural networks, Shallow neural networks, Recurrent artificial neural networks, Forecasting, Time series analysis","lastPublishedDoi":"10.21203/rs.3.rs-8592450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8592450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to their flexible model structures and their success in nonlinear modelling, artificial neural networks provide good alternatives for solving the forecasting problem. 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