Directional Forecasting of WTI and Brent Crude Oil Prices: A Machine Learning Approach with Technical Indicators at Daily, Weekly, and Monthly Frequencies

Directional Forecasting of WTI and Brent Crude Oil Prices: A Machine Learning Approach with Technical Indicators at Daily, Weekly, and Monthly Frequencies | 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 Directional Forecasting of WTI and Brent Crude Oil Prices: A Machine Learning Approach with Technical Indicators at Daily, Weekly, and Monthly Frequencies Badr Alnssyan, Muhammad Ali, Muhammad Ahmad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8253110/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 28 You are reading this latest preprint version Abstract Crude oil prices exhibit pronounced volatility, nonstationarity, and nonlinear behavior, making accurate forecasting inherently challenging, particularly when employing traditional statistical models such as the Autoregressive Integrated Moving Average (ARIMA) model. Although classical time-series techniques remain widely adopted by market practitioners, technical indicators have received comparatively limited attention in the academic literature on energy price forecasting. To address this research gap, the present study employs supervised machine learning algorithms—including Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Decision Tree (DT), Naïve Bayes (NB), and Random Forest (RF)—to forecast the directional movement (up or down) of two major crude oil benchmarks, West Texas Intermediate (WTI) and Brent, across three temporal frequencies: daily, weekly, and monthly, for the period 2010 to 2024. While these algorithms are capable of solving both regression and classification problems, this research specifically formulates crude oil price forecasting as a binary classification task, wherein the target variable indicates whether the price will rise or decline in the subsequent time interval. Model performance is assessed using four widely accepted classification metrics: accuracy, precision, recall, and F1-score. Empirical results demonstrate that SVM models—particularly those employing linear and polynomial kernels—consistently achieve superior forecasting accuracy compared to other classifiers across most experimental settings. Statistical models ARIMA Machine learning WTI Brent oil Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 03 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 29 Dec, 2025 Editor assigned by journal 14 Dec, 2025 Submission checks completed at journal 11 Dec, 2025 First submitted to journal 01 Dec, 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. 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. 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