ارائه مدلی برای پیش‌بینی قیمت جهانی نفت خام بر اساس الگوریتم هوش مصنوعی

مقالات آماده انتشار

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه اقتصاد، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

پیش بینی روند قیمت نفت خام و نوسانات آن همواره یکی از چالش های پیش روی معامله گران در بازارهای نفت بوده است. در این راستا، به منظور پیش‌بینی منطقی در مورد آینده، تلاش شده است تا روش‌های پیش‌بینی کمی و کیفی ایجاد و گسترش یابد. بنابراین روش های پیش بینی همواره ابزار مهمی برای محققین آینده بوده است. در نتیجه، این مطالعه با هدف بررسی و ارزیابی الگوریتم‌ها، به‌ویژه الگوریتم‌های یادگیری ماشین (مانند شبکه‌های بیزی، شبکه‌های عصبی مصنوعی، ماشین‌های بردار پشتیبان، k-نزدیک‌ترین همسایگان)، برای پیش‌بینی قیمت نفت خام است. برای این تحقیق، متغیرهایی مانند قیمت نفت اتحادیه اروپا و قیمت نفت ایالات متحده از سال 1990 تا 2020 در نظر گرفته شده است. برای تجزیه و تحلیل از نرم افزار پایتون استفاده شد.نتایج این مطالعه نشان داد که بر اساس الگوریتم های یادگیری ماشینی مورد استفاده در مدل اول (قیمت نفت خام برنت)، الگوریتم هایی مانند رگرسیون خطی، رگرسیون کمترین زاویه، تعقیب تطبیق متعامد و بیزی ریج. بهترین الگوریتم ها برای پیش بینی قیمت نفت خام برنت هستند. علاوه بر این، نتایج الگوریتم‌های یادگیری ماشین در مدل دوم (قیمت جهانی نفت برنت و قیمت نفت تگزاس غربی) نشان می‌دهد که رگرسیون خطی، رگرسیون کمترین زاویه، تعقیب تطبیق متعامد و ریج بیزی بهترین الگوریتم‌ها برای پیش‌بینی قیمت نفت تگزاس غربی هستند.

کلیدواژه‌ها

عنوان مقاله [English]

Presenting a model for predicting global crude oil prices based on artificial intelligence algorithm

نویسندگان [English]

  • Atefeh heidari
  • Alireza Daghighiasli
  • Ebrahim abbassi
  • Marjan Damankeshideh
Department of Economics, Central Tehran Branch, Islamic Azad university, Tehran, Iran
چکیده [English]

Predicting the price trend of crude oil and its fluctuations has always been one of the challenges facing traders in oil markets. In this regard, in order to make rational predictions about the future, efforts have been made to establish and expand quantitative and qualitative forecasting methods. Therefore, forecasting methods have always been important tools for future researchers. Consequently, this study aims to investigate and evaluate algorithms, especially machine learning algorithms (such as Bayesian networks, artificial neural networks, support vector machines, k-nearest neighbors), for predicting crude oil prices. For this current research, variables such as European Union oil prices and U.S. oil prices from 1990 to 2020 are considered. Python software was used for analysis.The results of this study have shown that, based on the machine learning algorithms used in the first model (Brent crude oil price), algorithms like Linear Regression, Least Angle Regression, Orthogonal Matching Pursuit, and Bayesian Ridge are the best algorithms for predicting the Brent crude oil price. Additionally, the results of machine learning algorithms in the second model (global Brent oil price and West Texas oil price) indicate that Linear Regression, Least Angle Regression, Orthogonal Matching Pursuit, and Bayesian Ridge are the best algorithms for predicting West Texas oil prices.

کلیدواژه‌ها [English]

  • Oil price prediction
  • Machine learning
  • Artificial intelligence
  • Prediction algorithm
  • Economic growth

مراجع

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مقالات آماده انتشار، پذیرفته شده
انتشار آنلاین از تاریخ 01 فروردین 1403

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