Forecasting Gold Price using Hybrid Deep Neural Network LSTM-Autoencoder

Forecasting Gold Price using Hybrid Deep Neural Network LSTM-Autoencoder | 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 Forecasting Gold Price using Hybrid Deep Neural Network LSTM-Autoencoder Agampreet Saini, Rahul Kumar Singh, Puneet Sinha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6035148/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted 16 You are reading this latest preprint version Abstract Gold prices hold a significant place in the global economy as they reflect the economic health, which influences markets, investments, and currency values. Industries that rely on commodities, investors and decision-makers, need accurate forecasting gold prices. Existing models for gold price forecasting, struggle with overfitting, poor adaptability and difficulty in handling volatile or long term trend changes. For this research, various deep learning models were evaluated, which includes Long Short-Term Memory, Convolutional Neural Networks, hybrid LSTM-CNN. The proposed hybrid model of LSTM-Autoencoders, to predict the gold prices for the data taken from September 2000 to January 2024. We also examines the impact of external parameters such as US dollar price, silver price, and crude oil price on forecasting gold prices. Furthermore, a comparative analysis of these external parameters shows that, only gold prices as a prediction parameter yields the highest accuracy across various evaluation metrics. While the silver prices showed some association with the gold price prediction, crude oil had a comparatively low predictive value. Additionally the proposed LSTM-Autoencoders hybrid model has shown the highest accuracy outperforming the other models, while addressing the challenge of overfitting effectively. The results and findings from this study, aid in exploring the role of deep learning in financial time series domain, offering insights which contributes to financial analysts market strategist and economic forecasters. Gold Price Prediction LSTM CNN Autoencoders Hybrid Models Time Series Forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 26 Jun, 2025 Reviews received at journal 26 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviews received at journal 31 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 08 May, 2025 Editor invited by journal 02 May, 2025 Editor assigned by journal 22 Mar, 2025 Submission checks completed at journal 22 Mar, 2025 First submitted to journal 15 Feb, 2025 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-6035148","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454190573,"identity":"d993cb7c-feec-4d08-ac1a-4df8635f3625","order_by":0,"name":"Agampreet Saini","email":"data:image/png;base64,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","orcid":"","institution":"University of Petroleum and Energy Studies","correspondingAuthor":true,"prefix":"","firstName":"Agampreet","middleName":"","lastName":"Saini","suffix":""},{"id":454190576,"identity":"c5156e24-9a05-47f3-8b0b-ac414fefa4ed","order_by":1,"name":"Rahul Kumar Singh","email":"","orcid":"","institution":"University of Petroleum and Energy Studies","correspondingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"Kumar","lastName":"Singh","suffix":""},{"id":454190577,"identity":"422a8975-206f-4f6e-bf01-702397bc1bcd","order_by":2,"name":"Puneet Sinha","email":"","orcid":"","institution":"Chattergee Group","correspondingAuthor":false,"prefix":"","firstName":"Puneet","middleName":"","lastName":"Sinha","suffix":""}],"badges":[],"createdAt":"2025-02-15 07:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6035148/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6035148/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44163-025-00464-w","type":"published","date":"2025-10-22T16:17:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94490519,"identity":"30be79d6-9496-4110-800a-69606faa4b50","added_by":"auto","created_at":"2025-10-27 17:11:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":790570,"visible":true,"origin":"","legend":"","description":"","filename":"goldpriceupdated3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6035148/v1_covered_54f19231-23a8-41ea-bef9-dd2a4d94cb02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecasting Gold Price using Hybrid Deep Neural Network LSTM-Autoencoder","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":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gold Price Prediction, LSTM, CNN, Autoencoders, Hybrid Models, Time Series Forecasting","lastPublishedDoi":"10.21203/rs.3.rs-6035148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6035148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGold prices hold a significant place in the global economy as they reflect the economic health, which influences markets, investments, and currency values. 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