Crude Oil Price Forecasting Model Using Machine Learning

3463 Words Jul 3rd, 2015 14 Pages
Crude Oil Price Forecasting Model Using Machine Learning
Tapas Peshin1 and Nikolaos V. Sahinidis2
1Graduate Student, Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, USA tpeshin@andrew.cmu.edu 2John E. Swearingen Professor, Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, USA sahinidis@cmu.edu ABSTRACT
The impact of oil price on the social, economic, political and many other aspects of human life is substantial. Oil exemplifies a vital role in the world economy as the backbone and origin of numerous industries. In global markets, it is the most active and heavily traded commodity. Global oil prices have fallen sharply over the past few months, leading to significant revenue shortfalls in many energy exporting nations. From 2010 until mid-2014, world oil prices had been fairly stable. WTI crude oil has declined ~58% since the middle of June 2014. Brent crude oil has also declined ~69% from mid-June. Recently many studies have emerged to discuss the problem of predicting oil prices and seeking to access to the best outcomes. This paper focuses on the use of a data-driven approach to predict crude oil prices using Automated Learning of Algebraic Models for Optimization (ALAMO) and comparing its results with the tools and techniques used in the past.
Keywords: Crude Oil Price Prediction, ALAMO, Modeling, Surrogate Modeling (SUMO) Toolbox, Neural Network Time Series INTRODUCTION
Crude oil is the most important…

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