Enhancing long-term forex market forecasting using transformer-based time series models: A comparative analysis with ensemble tree methods

Enhancing long-term forex market forecasting using transformer-based time series models: A comparative analysis with ensemble tree methods | 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 Enhancing long-term forex market forecasting using transformer-based time series models: A comparative analysis with ensemble tree methods Bich-Ngoc. Mach, Hoa-Cuc. Nguyen, Thanh Q. Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7733769/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 Accurate long-term forecasting in the foreign exchange (Forex) market remains a challenging task due to the inherent volatility, nonlinearity, and periodic trends of currency data. This study proposes a novel forecasting framework that integrates state-of-the-art Transformer-based time series models—Informer, Autoformer, FEDformer, and PatchTST—to capture complex temporal patterns and improve predictive performance. Utilizing enriched historical data from Yahoo Finance with engineered technical indicators (RSI, ATR, SMA, slope features), we benchmark these models against classical ensemble learning approaches including random forest, gradient boosting machines (GBM), and XGBoost. Experimental evaluations demonstrate that Transformer models, particularly FEDformer and PatchTST, outperform ensemble baselines in multi-step forecasting horizons, reducing MAPE and RMSE by significant margins. Furthermore, attention-based mechanisms enhance model interpretability while offering superior generalization to volatility shifts. This study highlights the potential of deep Transformer architectures in advancing financial time series forecasting, offering robust decision-making tools for traders and market analysts. Transformer-based forecasting Forex market Informer Autoformer FEDformer PatchTST time series prediction ensemble learning long-term trends attention mechanisms Full Text Additional Declarations No competing interests reported. Supplementary Files floatimage1.png 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-7733769","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534237401,"identity":"3a35304f-2ecd-4da5-a53c-31f66e97de4b","order_by":0,"name":"Bich-Ngoc. Mach","email":"","orcid":"","institution":"Thu Dau Mot University","correspondingAuthor":false,"prefix":"","firstName":"Bich-Ngoc.","middleName":"","lastName":"Mach","suffix":""},{"id":534237403,"identity":"507053a6-2d43-4eb7-aa38-891ec55fb65f","order_by":1,"name":"Hoa-Cuc. 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Nguyen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYHACNiA+IIdgEwbMYC3GDGzMJGpJbCBaizl7/rEHP3fcSd8u33+A4UPZYQaD2w34tVj2PGY37D3zLHdnGzMD44xzQC13DuDXYnAjmU2Ct+1w7oZjzAzMQAaD5IwEwlok/7YdTjcAaflLrBZpoOEJYC2MQC38EoS0nHlsJi3bdthww7Fkg4M959J5CGs5nvhM8m3bYXmDwwcfPvhRZi3HRkgLAwOSggNAzENIPaqWUTAKRsEoGAVYAQBNw0Myx4RK/QAAAABJRU5ErkJggg==","orcid":"","institution":"Nguyen Tat Thanh University","correspondingAuthor":true,"prefix":"","firstName":"Thanh","middleName":"Q.","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2025-09-28 10:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7733769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7733769/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94598704,"identity":"f3248cbd-61bf-437c-b2d0-e160fe8aad0a","added_by":"auto","created_at":"2025-10-28 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