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A Deep Learning Ensemble Method for Forecasting Daily crude oil price Based on Snapshot Ensemble of Transformer Model

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Computer Systems Science & Engineering 2023年第7期46卷 929-950页

作者： Ahmed Fathalla Zakaria Alameer Mohamed Abbas Ahmed Ali Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt Mining and Petroleum Department Faculty of EngineeringAl-Azhar UniversityQenaEgypt School of Computer Science and Technology Central South UniversityChangshaHunanChina College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

The oil industries are an important part of a country’s *** crude oil’s price is influenced by a wide range of ***,how accurately can countries predict its behavior and what predictors to employ are two main *** this view,we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting *** suggested method is based on a deep learning snapshot ensemble method of the Transformer *** examine the superiority of the proposed model,this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries(OPEC)oil price *** results demonstrated the outperformance of the proposed method over statistical and machine learning *** precisely,the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA(1,1,1),ARIMA(0,1,1),autoregressive moving average(ARMA)(0,1),vector autoregression(VAR),random walk(RW),support vector machine(SVM),and random forests(RF)models by 99.94%,99.62%,99.87%,99.65%,7.55%,98.38%,and 99.35%,respectively,according to mean square error metric.

关键词： Deep learning ensemble learning transformer model crude oil price

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Forecasting the crude oil price with Extreme Values

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Journal of Systems Science and Information 2014年第3期2卷 193-205页

作者： Haibin XIE Mo ZHOU Yi HU Mei YU School of Management University of International Business and Economics Research Center of Applied Finance University of Chinese Academy of Sciences

Extreme values are usually given special attention. Using a decomposition-based vector autoregressive(VAR) model, this paper investigates the additional information of extreme values for forecasting the crude oil price. Empirical studies performed on the WTI spot crude oil price over year 1986-2013 are positive: decomposition-based VAR model produces significant both in-sample and out-of-sample forecast. Different evaluation tests are used and the results unanimously report the dominance of decomposition-based VAR over both efficient market model and ARIMA model. These findings are important as they hint that forecasts can be improved if high-low extreme information is properly used. An even more interesting finding is that the predictability of the crude oil price is asymmetric: crude oil price is more predictable in recession than in expansion. This finding is of great significance as it means there is information friction in the oil market especially when the oil price is in recession.

关键词： forecast extreme values VAR crude oil price information friction

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RESEARCH ARTICLE Sensitivity of crude oil price Change to Major Global Factors and to Russian-Ukraine War Crisis

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Journal of Sustainable Business and Economics 2022年第2期5卷 69-75页

作者： Ibrahim A.Onour Mai M.Abdo Department of Business Administration School of Management StudiesUniversity of KhartoumSudan Department of Accounting&Finance School of Management StudiesUniversity of KhartoumSudan

To assess the elasticity of crude oil price to global factors related to supply of crude oil and the US dollar exchange rate, the authors employed nonlinear models including flexible least squares, and maximum likelihood estimator, in addition to OLS regression mode;using yearly data from 1965 to 2021. The findings indicate change in oil prices due to 1% change in any of the explanatory variables, as follows: the effect of the US dollar depreciation rate, raise crude oil price/barrel by 71 US cents;and increase in OPEC production, decrease crude oil price by 82 US cents;a decrease in non-OPEC production, raise oil price by 4.78 US$. These results imply that, if a ban imposed on Russian crude oil export, and no increase in OPEC production to compensate Russian oil loss in the international markets, global crude oil price expected to rise by 88 US$ above its level before Russian-Ukraine crisis, meaning that crude oil price expected to rise at 160 US$ pbab. However, if OPEC members increase their output level by 10 million barrels per day to compensate the Russian oil loss, then global crude oil price is expected to stay at 102 US$ pb.

关键词： OPEC crude oil price Russian and Ukraine crisis

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Improved crude oil price Forecasting With Statistical Learning Methods

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Journal of Modern Accounting and Auditing 2015年第1期11卷 51-62页

作者： Chokri Slim Manouba University Manouba Tunisia

Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method （SLM） based on combined fuzzy system （FS）, artificial neural network （ANN）, and support vector regression （SVR） to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate （WT1） crude oil spot price is used to test the effectiveness of the proposed learning methodology. Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.

关键词： crude oil price fuzzy system (FS) artificial neural networks (ANNs) support vector regression (SVR)

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An Econometric Modeling Approach to Short-term crude oil price Forecasting

An Econometric Modeling Approach to Short-term Crude Oil Pri...

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第三十届中国控制会议

作者： LI Weiqi~1,MA Linwei~2,DAI Yaping~1 LIU Pei~2 1.School of Automation,Beijing Institute of Technology University,Beijing 100081,P.R.China 2.State Key Laboratory of Power Systems,Department of Thermal Engineering,Tsinghua-BP Clean Energy Center, Tsinghua University,Beijing 100084,P.R.China

＜正＞In the competitive petroleum markets,oil price forecasting is becoming increasingly relevant to producers and *** paper develops a structural econometric model of the Brent crude spot price using the explanatory variable of defined relative inventory and OPEC production to analyze and forecast short-run oil price.A Hodrick-Prescott filter method presented obtains the relative inventory variables caused by the short-run supply and demand fluctuations in the crude oil market.A case study using the proposed method is provided,and the results indicate that the model developed in this work is helpful to industry and government in making oil-related decisions and investigating the changes in inventory and OPEC production on price.

关键词： crude oil price Econometric Model Forecasting

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Inflationary effects of oil prices and domestic gasoline prices:Markov-switching-VAR analysis

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Petroleum Science 2015年第2期12卷 355-365页

作者： Selin Ozdemir Isil Akgul Department of Econometrics Faculty of EconomicsMarmara University

The purpose of this study is to contribute to the literature by studying the effects of sudden changes both on crude oil import price and domestic gasoline price on inflation for Turkey, an emerging country. Since an inflation targeting regime is being carried out by the Central Bank of Turkey, determination of such effects is becoming more important. Therefore empirical evidence in this paper will serve as guidance for those countries, which have an in- flation targeting regime. Analyses have been done in the period of October 2005-December 2012 by Markovswitching vector autoregressive （MS-VAR） models which are successful in capturing the nonlinear properties of variables. Using MS-VAR analysis, it is found that there are 2 regimes in the analysis period. Furthermore, regime changes can be dated and the turning points of economic cycles can be determined. In addition, it is found that the effect of the changes in crude oil and domestic gasoline prices on consumer prices and core inflation is not the same under different regimes. Moreover, the sudden increase in gasoline price is more important for consumer price infla- tion than crude oil price shocks. Another finding is the presence of a pass-through effect from oil price and ga- soline price to core inflation.

关键词： crude oil price Domestic gasoline price Consumer price index - Core inflation MS-VAR model

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“石油—美元”动态关联的时变特征及影响因素研究

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世界经济导刊 2021年第1期

作者：王盼盼夏婷石建勋何宗武

本文基于原油的商品和金融双重属性，系统剖析“石油—美元”动态关联的理论机制，并采用DCC-GARCH和门限回归方法实证检验“石油—美元”动态关联的时变特征及影响因素。结果表明：第一，“石油—美元”联动关系具有明显的时变特征，2... 详细信息

本文基于原油的商品和金融双重属性，系统剖析“石油—美元”动态关联的理论机制，并采用DCC-GARCH和门限回归方法实证检验“石油—美元”动态关联的时变特征及影响因素。结果表明：第一，“石油—美元”联动关系具有明显的时变特征，2000年以前相关系数在0值上下波动，2000年以后则长期显著为负。第二，原油金融属性增强、美元资产价格上涨和全球金融市场风险厌恶水平上升会产生“石油—美元”负向联动关系。第三，2000年以后原油金融属性大幅增强，既凸显了美元基于价值贮藏作用对油价的直接负向影响，又进一步增强了“石油—美元”间基于美元资产价格和金融市场风险厌恶水平变动的间接负向联动机制，成为导致“石油—美元”联动关系长期显著为负的主要原因。本文的研究对深入理解国际原油价格波动机理，以及科学研判油价走势并制定有效的能源风险管理策略具有重要启示。

关键词：美元汇率原油价格动态关联 DCC-GARCH 门限回归 dollar currency rate crude oil price dynamic correlation DCC-GARCH threshold regression

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