Novel efficient reservoir computing methodologies for regular and irregular time series classification

Novel efficient reservoir computing methodologies for regular and irregular time series classification | 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 Research Article Novel efficient reservoir computing methodologies for regular and irregular time series classification Zonglun Li, Andrey Andreev, Alexander Hramov, Oleg Blyuss, Alexey Zaikin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4206717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Sep, 2024 Read the published version in Nonlinear Dynamics → Version 1 posted 12 You are reading this latest preprint version Abstract Time series is a data structure prevalent in a wide range of fields such as healthcare, finance and meteorology. Analyzing time series data holds the key to gaining insight into our day-to-day observations and among them, time series classification offers the unique opportunity to classify the sequences into their respective categories for the sake of automated detection. To this end, two types of mainstream approaches, recurrent neural networks and distance-based methods, have been commonly employed to address this specific problem. However, the most successful ones such as Long Short-Term Memory networks generally suffer substantially high computational demand, prompting the search for more efficient alternatives to reduce energy costs. Reservoir computing is an instance of recurrent neural networks that is known for its efficiency in processing time series sequences. Therefore, in this article, we will develop two reservoir computing based methods that can effectively deal with time series of different types with minimal computational cost, all while achieving a desirable level of classification accuracy. Mathematics Subject Classification (2020) 34A34 · 62P10 · 68T07 Reservoir computing Echo state networks Nonlinear dynamical systems Time series classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Sep, 2024 Read the published version in Nonlinear Dynamics → Version 1 posted Editorial decision: Revision requested 27 Jul, 2024 Reviews received at journal 26 Jul, 2024 Reviews received at journal 23 Jul, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviews received at journal 25 Jun, 2024 Reviewers agreed at journal 09 Apr, 2024 Reviewers agreed at journal 09 Apr, 2024 Reviewers invited by journal 08 Apr, 2024 Editor assigned by journal 04 Apr, 2024 Submission checks completed at journal 04 Apr, 2024 First submitted to journal 02 Apr, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4206717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287670788,"identity":"3116a9ef-ec0e-4395-bc5b-6e9547b5b3f0","order_by":0,"name":"Zonglun Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYLCCB0DMx8zA+CChAsiAiCXg1wKSZmNmYDb4cAbIIF4LEEnObCNCi3x77+EXiW13GNjYGRikeecdlmdjYH74gbEtDacWxp5zaRaJbc9ADmMw5t122LCNgc1YgrEtB6cWZokcM4PEtsNgLclALQlAh5kxMLZV4NTChqzlMO8ckBb2b3i18EjkGD+AamFsnNkA0sIDsgW3wyR4zpgxJJw7zMPGzNjM8OFYumEbM0+xRMI53N6Xb+8x/vCh7LAcP//h4z8Saqzl+dnbNwJFknFqAXsH5EBg4DVAQ4SBYEQyf8AvPwpGwSgYBSMeAADHM0RUiXVcaQAAAABJRU5ErkJggg==","orcid":"","institution":"University College London","correspondingAuthor":true,"prefix":"","firstName":"Zonglun","middleName":"","lastName":"Li","suffix":""},{"id":287670789,"identity":"c36da12c-dd62-4b2c-b2bd-6e7cb885e52f","order_by":1,"name":"Andrey Andreev","email":"","orcid":"","institution":"Immanuel Kant Baltic Federal University","correspondingAuthor":false,"prefix":"","firstName":"Andrey","middleName":"","lastName":"Andreev","suffix":""},{"id":287670792,"identity":"c6a3e7dd-89d7-4db1-8ef1-c163337e0ba2","order_by":2,"name":"Alexander Hramov","email":"","orcid":"","institution":"Immanuel Kant Baltic Federal University","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Hramov","suffix":""},{"id":287670794,"identity":"d57e3351-c6b7-470d-ba51-2160cf234786","order_by":3,"name":"Oleg Blyuss","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Oleg","middleName":"","lastName":"Blyuss","suffix":""},{"id":287670797,"identity":"00d9634d-5aeb-4c60-81d0-443636d0c99e","order_by":4,"name":"Alexey Zaikin","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Alexey","middleName":"","lastName":"Zaikin","suffix":""}],"badges":[],"createdAt":"2024-04-02 12:35:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4206717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4206717/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11071-024-10244-3","type":"published","date":"2024-09-06T16:05:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64185811,"identity":"f6bdf1d6-55ae-4cf0-9bc9-3163426330bb","added_by":"auto","created_at":"2024-09-09 16:22:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":628055,"visible":true,"origin":"","legend":"","description":"","filename":"Novelefficientreservoircomputingmethodologiesforregularandirregulartimeseriesclassification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4206717/v1_covered_1406dca7-c96f-457a-9965-d2bab7dfde91.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel efficient reservoir computing methodologies for regular and irregular time series classification","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":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Reservoir computing, Echo state networks, Nonlinear dynamical systems, Time series classification","lastPublishedDoi":"10.21203/rs.3.rs-4206717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4206717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTime series is a data structure prevalent in a wide range of fields such as healthcare, finance and meteorology. 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