Time Series Prediction using Laplace Transform-based Deep RNN- LSTM Approach

Time Series Prediction using Laplace Transform-based Deep RNN- LSTM Approach | 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 Time Series Prediction using Laplace Transform-based Deep RNN- LSTM Approach C. Ambhika, Raja Chandrasekaran, Sreenivasulu Bolla, Amit Gangopadhyay, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4891456/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents a novel hybrid framework employing integrated Laplace transform-based Deep Recurrent Neural Network-Long shortTerm memory (DRNN-LSTM) model for time series prediction. In the presented work, the Ebola Optimization search (EOS) optimization algorithm was utilized to train the unsupervised DRNN-LSTM model. The Laplace transform in the proposed work eliminates the noises or unwanted features and helps to transform the input signal into a required format. Finally, the experimental analysis was performed and validated with the conventional techniques. The experimental analysis demonstrates that the designed model effectively eliminates the noises and also extracts the features accurately and also outperformed all the state-of-art techniques. Timeseries denoising training RNN-LSTM Laplacetransform Full Text Additional Declarations No competing interests reported. Supplementary Files Authorshipchangeform.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4891456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338623518,"identity":"967aeaa7-20f4-435d-8541-6a313a62e071","order_by":0,"name":"C. 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