Applying dual attention deep learning model to predict oil futures prices

Applying dual attention deep learning model to predict oil futures prices | 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 Applying dual attention deep learning model to predict oil futures prices Hsin-Chun Yen, Wen-Chen Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1136379/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 Due to the non-linear and complex characteristics of oil futures prices, they are easily affected by external factors. Therefore, predicting the price of oil futures is a very challenging topic in deep learning time series, but the existing literature lacks research on introducing relevant features. Therefore, the purpose of this paper is to use a two-way attention mechanism to predict oil futures prices and import gold prices as features. The data source is taken from Yahoo Finance, providing 5146 items from 2000/02/28 to 2020/06/29. The paper uses CNNBiLSTM, CNNBiGRU models and Attention CNNBiLSTM, Attention CNNBiGRU, Dual Attention CNNBiGRU, Dual Attention CNNBiLSTM and TPA-LSTM, Prophet, ARIMA that increase the attention mechanism to conduct experiments to find the best model. Moreover, explore the sliding window review days to deal with two-dimensional time series data and the influence of the delay of the typical days to predict the experiment's impact. The experimental results show that increasing the number of review days will decrease the prediction accuracy. The new model is imported using the two-dimensional time series method, and the attention mechanism is integrated. Importing CNNBiGRU with a dual attention mechanism from 80% of the training items and 20% of the test items in the experimental data set can improve the overall prediction accuracy. For example, the model BiGRU has improved the RMSE from 2.79 to 1.85 by adding a dual attention mechanism. In addition, it is found that if the regression model has large fluctuations in the prediction data, it will increase the RMSE and MAE, especially in the experimental items of 96% training and 4% testing data sets. Dual Attention CNNBiLSTM CNNBiGRU RMSE deep learning sliding window Prophet TPA-LSTM ARIMA Full Text Supplementary Files 1203dualattentationAuthor.pdf 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-1136379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":140705468,"identity":"59b0d0de-eb7e-4242-bf50-a3365a04220c","order_by":0,"name":"Hsin-Chun Yen","email":"","orcid":"","institution":"National Kaohsiung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hsin-Chun","middleName":"","lastName":"Yen","suffix":""},{"id":140705469,"identity":"064c670d-6e75-427c-b235-789a2ba61106","order_by":1,"name":"Wen-Chen Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYHACNoYPDAkQJg+xWhhngLSwQbRIEKWFmYckLQbHe489tt2Rljh/fgPjg7dtDHUGBwhpOXMu3Tj3TE7ihmMMzIZz2xgkCGoxu5FjJp3bVpG4gY2BTZoXqMWMoJb7b8ykLYFa5rcxsP8mTssNHjNpxracxIZjwHAgSov9mRwzyd62NOMNxxKbJeeck5DcT0iLZPsZM4mfbcmy85sPH/zwpsyGX7KBgBYkwAhSS1RMjoJRMApGwSggBADaajxw6lWnOwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9389-8865","institution":"National Kaohsiung University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Wen-Chen","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2021-12-03 02:26:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1136379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1136379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27328529,"identity":"06514af9-c6df-492f-af5d-1dd134f90c13","added_by":"auto","created_at":"2022-10-04 15:18:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1078760,"visible":true,"origin":"","legend":"","description":"","filename":"1203dualattentation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1136379/v1_covered.pdf"},{"id":27328525,"identity":"29267485-e013-4cae-9f2d-e14a7bee90ed","added_by":"auto","created_at":"2022-10-04 15:18:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":320571,"visible":true,"origin":"","legend":"","description":"","filename":"1203dualattentationAuthor.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1136379/v1/2b53cf3d5ff8999a449ebdc6.pdf"}],"financialInterests":"","formattedTitle":"Applying dual attention deep learning model to predict oil futures prices","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1136379/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dual Attention, CNNBiLSTM, CNNBiGRU, RMSE, deep learning, sliding window, Prophet, TPA-LSTM, ARIMA","lastPublishedDoi":"10.21203/rs.3.rs-1136379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1136379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the non-linear and complex characteristics of oil futures prices, they are easily affected by external factors. Therefore, predicting the price of oil futures is a very challenging topic in deep learning time series, but the existing literature lacks research on introducing relevant features. Therefore, the purpose of this paper is to use a two-way attention mechanism to predict oil futures prices and import gold prices as features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data source is taken from Yahoo Finance, providing 5146 items from 2000/02/28 to 2020/06/29. The paper uses CNNBiLSTM, CNNBiGRU models and Attention CNNBiLSTM, Attention CNNBiGRU, Dual Attention CNNBiGRU, Dual Attention CNNBiLSTM and TPA-LSTM, Prophet, ARIMA that increase the attention mechanism to conduct experiments to find the best model. Moreover, explore the sliding window review days to deal with two-dimensional time series data and the influence of the delay of the typical days to predict the experiment's impact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experimental results show that increasing the number of review days will decrease the prediction accuracy. The new model is imported using the two-dimensional time series method, and the attention mechanism is integrated. Importing CNNBiGRU with a dual attention mechanism from 80% of the training items and 20% of the test items in the experimental data set can improve the overall prediction accuracy. For example, the model BiGRU has improved the RMSE from 2.79 to 1.85 by adding a dual attention mechanism. In addition, it is found that if the regression model has large fluctuations in the prediction data, it will increase the RMSE and MAE, especially in the experimental items of 96% training and 4% testing data sets.\u003c/p\u003e","manuscriptTitle":"Applying dual attention deep learning model to predict oil futures prices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-04 15:18:35","doi":"10.21203/rs.3.rs-1136379/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"651eac10-661f-4b68-bdb6-8dbc458eb5ac","owner":[],"postedDate":"October 4th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-19T15:11:52+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-04 15:18:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1136379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1136379","identity":"rs-1136379","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}