scholarly journals The Optimal Machine Learning Modeling of Brent Crude Oil Price

2021 ◽  
Vol 7 (1) ◽  
pp. 31-43
Author(s):  
Chukwudi Paul Obite ◽  
Desmond Chekwube Bartholomew ◽  
Ugochinyere Ihuoma Nwosu ◽  
Gladys Ezenwanyi Esiaba ◽  
Lawrence Chizoba Kiwu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Crude Oil ◽  
Global Economy ◽  
Arima Model ◽  
Oil Price ◽  
Support Vector ◽  
Energy Information Administration ◽  
Close Monitoring ◽  
Crude Oil Price

The price of Brent crude oil is very important to the global economy as it has a huge influence and serves as one of the benchmarks in how other countries and organizations value their crude oil. Few original studies on modeling the Brent crude oil price used predominantly different classical models but the application of machine learning methods in modeling the Brent crude oil price has been grossly understudied. In this study, we identified the optimal MLMD (MLMD) amongst the Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), and Deep Neural Network (DNN) in modeling the Brent crude oil price and also showed that the optimal MLMD is a better fit to the Brent crude oil price than the classical Autoregressive Integrated Moving Average (ARIMA) model that has been used in original studies. Daily secondary data from the U.S. Energy Information Administration were used in this study. The results showed that the ANN and DNN models behaved alike and both outperformed the SVR and RF models and are chosen as the optimal MLMDs in modeling the Brent crude oil price. The ANN was also better than the classical ARIMA model that performed very poorly. The ANN and DNN models are therefore suggested for a close monitoring of the Brent crude oil price and also for a pre-knowledge of future Brent crude oil price changes.

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

scholarly journals Daqing Crude Oil Price Forecast Based on the ARIMA Model

2015 ◽  
Vol 8 (1) ◽  
pp. 457-462
Author(s):  
Li Quan
Keyword(s):  
Crude Oil ◽  
Arima Model ◽  
Oil Prices ◽  
Industrial Country ◽  
Oil Price ◽  
Crude Oil Price ◽  
Major Industrial Country ◽  
Price Forecast ◽  
Industrial Economy ◽  
Monthly Average

Oil is the lifeblood of the industrial economy, oil prices are affected by many factors. China is a major industrial country, changes in the price of oil will affect many aspects of economic development, and therefore the price of crude oil research is extremely important. In this paper, monthly average prices of crude oil in Daqing from January 2000 to December 2010 are utilized to do the research. Based on ARIMA model by building software using EVIEWS, rule of oil price movements is found and a prediction of oil price is made using the data from the first 10 months of 2011.

Data Mining Methods for Crude Oil Market Analysis and Forecast

2010 ◽  
pp. 184-203 ◽  
Author(s):  
Jue Wang ◽  
Wei Xu ◽  
Xun Zhang ◽  
Yejing Bao ◽  
Ye Pang ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Crude Oil ◽  
Nonlinear Models ◽  
Oil Price ◽  
Support Vector ◽  
Public And Private ◽  
Governmental Agencies ◽  
Artificial Neural Network Ann ◽  
Price Analysis

In this study, two data mining based models are proposed for crude oil price analysis and forecasting, one of which is a hybrid wavelet decomposition and support vector Machine (SVM) model and the other is an OECD petroleum inventory levels based wavelet neural network model (WNN). These models utilize support vector regression (SVR) and artificial neural network (ANN) technique for crude oil prediction and are made comparison with other forecasting models, respectively. Empirical results show that the proposed nonlinear models can improve the performance of oil price forecasting. The findings of this research are useful for private organizations and governmental agencies to take either preventive or corrective actions to reduce the impact of large fluctuation in crude oil markets, and demonstrate that the implications of data mining in public and private sectors and government agencies are promising for analyzing and predicting on the basis of data.

scholarly journals Crude Oil Price Forecasting Using Hybrid Support Vector Machine

2020 ◽  
Vol 917 ◽  
pp. 012045
Author(s):  
Lee Jo Xian ◽  
Shuhaida Ismail ◽  
Aida Mustapha ◽  
Mohd Helmy Abd Wahab ◽  
Syed Zulkarnain Syed Idrus
Keyword(s):  
Support Vector Machine ◽  
Crude Oil ◽  
Oil Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Crude Oil Price

Application of ARIMA Model in Short-Term Prediction of International Crude Oil Price

2013 ◽  
Vol 798-799 ◽  
pp. 979-982 ◽  
Author(s):  
Ying Xiang ◽  
Xiao Hong Zhuang
Keyword(s):  
Crude Oil ◽  
Arima Model ◽  
Oil Price ◽  
Good Prediction ◽  
Crude Oil Price ◽  
Short Term ◽  
Refined Oil ◽  
Price Data ◽  
Term Prediction ◽  
Short Term Prediction

International crude oil price is the referential scale of spot crude oil price and refined oil price. This paper made an analysis and prediction of Brent crude oil price by ARIMA model based on its price data from November 2012 to April 2013. It indicated that model ARIMA (1,1,1) possessed good prediction effect and can be used as short-term prediction of International crude oil price.

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