Explainability and Importance Estimate of TimeSeries Classifier via Embedded Neural Network

Explainability and Importance Estimate of TimeSeries Classifier via Embedded Neural Network | 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 Explainability and Importance Estimate of TimeSeries Classifier via Embedded Neural Network Ho Tung Jeremy Chan, Ilija Šimić, Eduardo Veas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4556811/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Time Series is sequential data with temporal relationship of causality. Time Series can be univariate, a single ordered data sequence, or multivariate, a collection of more than one ordered data sequences. Multivariate Time Series are complex with inter- and intra-relationship between the ordered data sequences. Time Series is common across disciplines and can benefit from insightful analysis.However, analysis of interpretation and importance estimate within a Time Series context is scarce, especially Neural Network (NN) related approaches.In this work, we demonstrate NN related approaches upon Time Series with the aim of explainability and importance estimate of embedded NN classifers. We conducted an empirical study with univariate and multivariate Time Series, where we compared interpretation and importance estimate from existing embedded NN approaches, an explainable AI (xAI) approach, and our adapted method of Pairwise Importance Estimate Extension from previously. We verified interpretation and importance estiamte via ground truth when it is available, or via a combined approach of Sensitivity Analysis and Reduce and Retrain, where we Retrain with Leave-One-Out and Singleton subsets. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Reviews received at journal 24 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviewers agreed at journal 13 Jun, 2024 Reviewers agreed at journal 13 Jun, 2024 Reviewers invited by journal 12 Jun, 2024 Editor assigned by journal 12 Jun, 2024 Editor invited by journal 12 Jun, 2024 Submission checks completed at journal 11 Jun, 2024 First submitted to journal 10 Jun, 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. We do this by developing innovative software and high quality services for the global research community. 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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-4556811","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":318077004,"identity":"f62dbb1d-d815-4d71-b126-bd4731dec601","order_by":0,"name":"Ho Tung Jeremy Chan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACxgYeIFmRQLKWM3AtBhBB/JqAWhjbSNHC3H724OfCeWl5/BIJjB8+7vgjZ97A/PwBXof15CVLz9yWUyw5I4FZcuYZA2OZA2yGeG1hnMFjIM27rSJxw+38b8y8bQaJMxgYCGox/s07B6QlgY35b5tB/QwG9o+EtJhJ8zbkQLQwthkkSDDwELClJ8fMmudYWuLM+Q+YJXvbjA1nMPMUzsCnxbD9jPFtnprkxH6eA4wffrbJyUuwt2/4gFcLphuY8akHAnkC8qNgFIyCUTAKGBgAd9BEUZLgLnMAAAAASUVORK5CYII=","orcid":"","institution":"Know Center","correspondingAuthor":true,"prefix":"","firstName":"Ho","middleName":"Tung Jeremy","lastName":"Chan","suffix":""},{"id":318077005,"identity":"e2a44d7b-9f07-4e13-99ce-a7b4fb3429cd","order_by":1,"name":"Ilija Šimić","email":"","orcid":"","institution":"Know Center","correspondingAuthor":false,"prefix":"","firstName":"Ilija","middleName":"","lastName":"Šimić","suffix":""},{"id":318077006,"identity":"9ce46c6d-be91-44b9-a8cb-bbac95274df6","order_by":2,"name":"Eduardo Veas","email":"","orcid":"","institution":"Graz University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"","lastName":"Veas","suffix":""}],"badges":[],"createdAt":"2024-06-10 08:46:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4556811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4556811/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-17703-w","type":"published","date":"2025-10-03T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92885116,"identity":"7c2904d6-5aec-44fa-a273-79d33c06dd01","added_by":"auto","created_at":"2025-10-06 16:14:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6279364,"visible":true,"origin":"","legend":"","description":"","filename":"ExplainabilityandImportanceEstimateofTimeSeriesClassifierviaEmbeddedNeuralNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4556811/v1_covered_807c935d-fdc4-4b81-ba77-844d0a591352.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainability and Importance Estimate of TimeSeries Classifier via Embedded Neural Network","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4556811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4556811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Time Series is sequential data with temporal relationship of causality. 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We verified interpretation and importance estiamte via ground truth when it is available, or via a combined approach of Sensitivity Analysis and Reduce and Retrain, where we Retrain with Leave-One-Out and Singleton subsets. 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