Crude Oil Price Prediction Research Articles

Potential formation mechanism and prediction of crude oil price based on underdetermined independent component analysis

In most crude oil price prediction, a few of them explored the potential formation mechanism of oil price. This study proposes a novel multi-scale integrated (MSI) learning paradigm to mine the potential formation mechanism of the crude oil price and predict the crude oil price. Firstly, we propose a novel underdetermined independent component analysis (UICA) model based on extreme-point symmetric mode decomposition (ESMD) and independent component analysis (ICA) to study the potential formation mechanism of crude oil price. Based on the representative independent components (ICs), we found and analyzed some factors affecting the crude oil price from high frequency to low frequency, as well as the potential formation mechanism of crude oil price, such as geopolitical implications, natural disasters, historical pricing data and copper futures price. Moreover, the ICs are predicted and generated the final forecasting result of crude oil price via radial basis function neural network (RBFNN). The experimental results based on the West Texas Intermediate (WTI) crude oil price, confirm that the proposed MSI model is superior to the existing prediction models in terms of statistical and error performance criteria and Diebold-Mariano (DM) test with the optimal D stat = 99.0220 , MAE = 0.0050 , RMSE = 0.0537 , MAPE = 0.0116 , ESD = 0.0007 , R 2 = 0.9960 .

Hybrid Prediction Model for International Crude Oil Prices Based on EEMD and SE Reconstruction Methods

Accurate prediction of crude oil prices can provide references for the energy economy and valuable guidance for investors and decision-makers in formulating strategies. However, due to the complexity and volatility of the crude oil market, achieving accurate and reliable crude oil price forecasts is challenging. Therefore, this paper proposes a hybrid forecasting model for crude oil prices that combines Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SE) reconstruction. Firstly, the daily closing price data of WTI crude oil futures from January 2, 2018, to March 21, 2024, is selected. The EEMD method is used to decompose the original data, and the decomposed sub-sequences are reconstructed and merged into high-frequency, mid-frequency, and low-frequency sequences using Sample Entropy (SE). Finally, these sequences are input into six machine learning models for prediction and comparison. The research results show that, firstly, by comparing the R2, RMSE, MAE, and MAPE values, it can be seen that the forecasting ability of the EEMD-SE hybrid forecasting model is significantly better than the EEMD hybrid forecasting model, indicating that the combination of SE methods can effectively improve the model s forecasting ability. Secondly, the EEMD-SE-LSTM model has higher prediction accuracy than other models. Finally, the comparison of models reveals that the BP neural network exhibits the greatest improvement after SE reconstruction. Therefore, it is recommended to improve the crude oil futures price forecasting and risk warning system.

Navigating the Dynamics of Brent Crude Oil Prices: Factors, Trends, and Insights

Accurate Brent Crude Oil price prediction is essential for informed decision-making and risk control in the global energy market. This paper examines the dynamics of Brent Crude Oil prices over the previous 35 years, utilizing predictive modelling techniques to project future prices and analyzing historical trends and relationships with the GDPs of major economies. The statistical methods used in this paper include the moving average and Pearson correlation coefficient. While correlation studies show significant positive links between Brent Crude Oil prices and the GDPs of major economies like China, Saudi Arabia, Europe, Russia, and the United States, historical data analysis reveals large price volatility. Highly accurate price forecasting using LSTM (Long Short-Term Memory) modelling approaches provides detailed risk management and decision-making information in the global energy market. The application of the Neural Network and the ARIMA model shows an increase in the price of Brent crude oil in the next year, 2024. Identifying the importance of the Brent crude oil price forecast and its effect on the international market is highly needed. This paper thus might help to increase economic growth. These results highlight the importance of advanced modelling techniques and economic indicators in managing oil price changes and help make informed decisions in the energy industry.

Modelling and Forecasting Crude Oil Prices Using Trend Analysis in a Binary-Temporal Representation

The oil market is one of the most important markets for the global economy. Often, oil prices influence the financial results of whole countries and sectors. Therefore, the modeling and prediction of crude oil prices are of high importance. Most up-to-date publications have used daily closing rates in crude oil price modeling, not considering the variability in prices during the day. The application of this kind of price representation leads to a loss of information about the range of price changes during the day, which influences the accuracy of the models and makes them useless in short-term course predictions. In this paper, we introduce the concept of a new state model in a binary-temporal representation, which uses trend analysis, which is one of the main methods used in the prediction of the direction of future changes in the course trajectory. The model described in this paper stands as the first tool that allows for predicting course changes in a given range. The presented work also summarizes the research results of modeling crude oil prices from the last six years, which prove the effectiveness of the mentioned modeling method.

Indonesian Crude Oil Price (ICP) Prediction Using Support Vector Regression Algorithm

Indonesian Crude Oil Price (ICP) Prediction Using Support Vector Regression Algorithm

Enhancing Economic Stability with Innovative Crude Oil Price Prediction and Policy Uncertainty Mitigation in USD Energy Stock Markets

In today’s globalized economic landscape, the assurance of economic stability is of paramount importance, necessitating precise financial decision-making and policy formulation. This assurance is significantly augmented by innovative approaches to predicting crude oil prices, particularly in the context of energy stock markets denominated in USD. This paper delves into the transformative effect of accurate crude oil price prediction on economic policy stability. It underscores the challenges and limitations posed by policy uncertainties and emphasizes the pivotal role of innovative solutions in mitigating these challenges. Moreover, it recognizes the imperative need for secure data storage to facilitate the application of machine learning in this domain. Furthermore, effective management and regulation of power grid systems are explored as indispensable strategies for tempering the volatility introduced by fluctuations in energy stock markets. As we work to address these gaps in knowledge, the potential for sustainable power systems to supersede fossil fuels emerges as a driving force behind the maintenance of stable economic policies.

Application of Cheng’s Fuzzy Time Series in World Crude Oil Price Prediction

Fuzzy time series is a new concept that can be used to predict an event using historical data. Historical data is processed using the principles and logic of fuzzy sets. The aim of this study is to predict world oil prices. The historical data used was from 3 January 2022 to 30 June 2023. This article discusses Cheng’s Fuzzy Time Series application. Determining the number of fuzzy class intervals uses 3 approaches, namely using the Sturges formula, Average-based and Partial Frequency Density. The 3 approaches used will be compared. Fuzzy Time Series with the Sturges formula produces a MAPE of 10.54% and a MSE of 9.13. Average-based Fuzzy Time Series produces a MAPE of 7.64% and a MSE of 5.15. Partial Frequency Density Fuzzy Time Series produces a MAPE of 8.09% and a MSE of 5.99. The results of this study state that Cheng’s Average based Fuzzy Time Series has the best accuracy in predicting world oil prices.

A Hybrid Deep Learning Approach for Crude Oil Price Prediction

Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). We compared our one-step CNN–LSTM model with other LSTM models, the CNN model, support vector machine (SVM), and the autoregressive integrated moving average (ARIMA) model. Also, we compared our multi-step CNN–LSTM model with LSTM, CNN, and the time series encoder–decoder model. Extensive experiments were conducted using short-, medium-, and long-term price data of one, five, and ten years, respectively. In terms of accuracy, the proposed model outperformed existing models in both one-step and multi-step predictions.

The Challenges and Countermeasures of Ideological and Political Education Theory Course Teaching for Art Students in Universities in the New Media Era

Contemporary art students in universities are a generation that has grown up in the context of new media. With the increasingly close integration of art and new media, in order to enable art students to better shoulder the mission of spreading the excellent culture of the Chinese nation, ideological and political course teachers in universities should strengthen the reform and innovation of ideological and political theory courses for art students in teaching content, teaching methods, and the use of new media, Ensure that art students embody a positive energy of thought in their artistic creation. In this study, a crude oil price prediction model based on quantum leap radius and polynomial fitting is established by building a market atom model, fitting financial funds as "market atoms" with energy, and applying quantum leap theory to describe the leaping behavior of price. The LSTM neural network model is used to learn the price jump paths of financial instruments to improve the prediction effect. After 2000 rounds of iterative training, the RMSE of the model reaches 3.1136, and the correlation coefficient is 0.48084; the model's loss function decreases rapidly, indicating that the training effect is good. The study reveals the potential value of quantum theory in finance, preliminarily verifies the application of quantum leap and LSTM model in crude oil price prediction, and provides new ideas for the in-depth study of quantum finance theory.

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

AbstractAs the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

Application of the geometric Brownian motion model in West Texas Intermediate crude oil price prediction

Crude oil is one of the primary commodities in the global economy. Crude oil prices are among the most complex and challenging to model because of the irregular, non-linear, non-stationary fluctuations in crude oil prices and their high volatility. It is essential to predict crude oil commodity prices to reduce the negative impact of fluctuations in crude oil commodity prices. Several mathematical models can be used to forecast crude oil commodity prices. One model that can be used is the Geometric Brownian Motion model, also known as the Wiener process. In this research, predictions for WTI (West Texas Intermediate) crude oil in 2022 were carried out using the Geometric Brownian Motion model. The results of this research are predictions of crude oil prices for July 2023 with iterations of 100, 200, and 1000, respectively, producing MAPE values of 6.092415%, 7.364198%, and 7.276606%.

Penerapan Deep Learning Menggunakan Gated Recurrent Unit Untuk Memprediksi Harga Minyak Mentah Dunia

Crude oil is a much-needed energy for the whole world. Each country is inseparable from the use of crude oil for use in various sectors, such as transportation, so that the price of world crude oil is the most important variable for the world. Fluctuations in oil prices will cause various problems, such as inflation, changes in market prices, and others. Therefore, the prediction of world crude oil prices is very important as a consideration for decision making. This study implements deep learning using the Gated Recurrent unit model. The data used is the price of Brent crude oil with a total of 5834 data, starting from January 4, 2000 to December 19, 2022. The parameters used are the number of GRU units, batch size, and lookback. The best model produced in this study is the GRU model with hyperparameters consisting of 30 lookbacks, 50 GRU units, and 256 batch sizes with the lowest MAPE value among the other models, which is 2.25%. The MAPE value states that predictions using the GRU model are said to be very good at predicting world crude oil prices

Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods

Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods

Do the US president's tweets better predict oil prices? An empirical examination using long short-term memory networks

ABSTRACT The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance.

Does the Sentiment Index Help Predict Crude Oil Prices?

The price fluctuations in the crude oil market remarkably influence the global economy since crude oil is an essential source of energy and plays a determinant role in most industrial sectors. The tremendous development of social media has generated many applications that can be used for sentiment analysis to improve the prediction of crude oil prices. Many researchers have also used technical indicators to predict oil prices. This study integrated several machine learning approaches—random forest, support vector machine, and long short-term memory—with a dynamic expanding moving window and fixed moving window to forecast one-period-ahead West Texas Intermediate (WTI) spot prices. We assessed the forecasting performance of these models using the root mean squared error and then compared prediction accuracy among the sentiment indicator, the technical indicator, and the lagged values of WTI spot prices using the Diebold–Mariano test. The forecasting simulation and empirical results show that the sentiment indicator is preferable to the technical indicator for forecasting WTI spot prices with machine learning approaches. Interestingly, we also find that using the sentiment indicator provides a better prediction performance than using lagged values of crude oil prices.

Crude Oil Price Prediction Based on Multiple Ensemble Learning Algorithms

The volatility of crude oil price affects people from all walks of life. Since the start of the 21st century, the frequent booms and busts in crude oil price have caused a greater emphasis on forecasting the price. In order to make prediction from a new perspective, this paper combined both financial and non-financial factors and applied both bagging and boosting algorithms in ensemble learning to construct models. According to the results, both bagging and boosting algorithms achieved impressive improvements over the benchmark model. The ensemble learning models can not only reach high goodness-of-fit, but also attain excellent accuracy in direction prediction, which indicates the models can well realize the long term trend prediction of crude oil price. In brief, this paper enriched the content in predicting crude oil price and offer a guideline for the application of ensemble learning algorithms in such a field.

Crude oil price prediction using deep reinforcement learning

Crude oil price forecasting has received considerable attention owing to its significance in the commodity market and non-linear complexity in forecasting tasks. This study aims to develop a novel deep reinforcement learning algorithm for multi-step ahead crude oil price forecasting in three major commodity exchanges. The proposed algorithm includes two main improvements: (a) A dynamic action exploration mechanism based on the stochastic processes conforming to commodity price fluctuations is designed for accuracy and generalization. (b) A dynamic update policy of network parameters based on approximate optimization theory is developed to improve the network's learning efficiency. The algorithm's effectiveness is experimentally verified and compared with five state-of-the-art algorithms. The main findings are as follows. (a) DRL's forecasting ability is developed in crude oil price forecasting, which may be extended to the forecasting of other natural resource prices. (b) The proposed algorithm can be applied to the data of the world's three major crude oil price benchmarks with considerable universality. (c) The accuracy of the proposed algorithm declines indistinctively with the expansion of the forecasting step; however, it reflects the actual price and fluctuation. These findings have implications in accelerating the global economic recovery and exploring AI in the energy market.

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions.

Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm

Crude oil prices play a significant role in the global economy, therefore accurate prediction of oil prices is very important. Therefore, a forecasting model is needed to predict Crude Oil Prices. The purpose of this study is to forecast the price of crude oil from Indonesia (ICP). The data source is from a website published by the Ministry of Energy and Mineral Resources (ESDM), namely monthly crude oil price data specifically for six main types of crude oil: SLC, Attaka, Duri, Belida, Banyu and SC. The data used is data for a period of 5 years (2018 – 2022). The data available is in the form of time series data. Dated Brent combined with the Alpha factor for each month and year is a reference in determining the ICP price. Forecasting Indonesian crude oil prices in the future is based on the historical oil price of the previous period. The Data Mining algorithm used for forecasting is Multiple Linear Regression. The dataset processed using training data is 80%, and testing data is 20%. The model produced, on average, has a good level of accuracy in calculating MAPE where for SLC = 9%, Attaka = 45%, Duri = 126%, Belida = 33%, Banyu = 150% and SC = 50%. Based on the MAPE calculation value, the Linear Regression Equation to predict Indonesian Crude Oil Prices (ICP) shows that the model produced by SLC crude oil is very good. Attaka, Belida and SC crude oil yielded fair yields and Duri and Banyu crude oil yielded poor yields.

High-frequency direction forecasting and simulation trading of the crude oil futures using Ichimoku KinkoHyo and Fuzzy Rough Set

With the rapid development of the Chinese economy in the last thirty years, crude oil has become an essential commodity and a strategic resource for China. Thus, the topic of crude oil price prediction has drawn significant attention from market participants and researchers. In this study, we propose an intelligent trading decision support system called FRS-NSGA-II-SW, which integrates the Fuzzy Rough Set (FRS), Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and Sliding Window (SW), for high-frequency price direction prediction and simulated trading of the crude oil futures in the Chinese market. Additionally, a Japanese technical indicator Ichimoku KinkoHyo is employed to extract features of high-frequency movement for the proposed model. To assess the performance comprehensively, four evaluation measures, including hit ratio, accumulated return, maximum drawdown, and Sharpe ratio, are employed to investigate the performances of the proposed method and benchmark methods. As a result, the proposed approach produced an average hit ratio of 66.84%, an average accumulated return of 20.39%, an average maximum drawdown of 8.38%, and a Sharpe ratio of 1.22, which outperformed all the benchmark methods. Additionally, Friedman test results indicate that the proposed method significantly outperformed the benchmark methods in terms of prediction accuracy and profit-making ability. Furthermore, experimental results show that integrating the Multi-objective optimization algorithm NSGA-II and the dynamic learning method SW successfully enhanced the performance of the proposed method. The excellent performance of the proposed method demonstrates that it could be applied as an intelligent trading decision support system for market investors and regulators in the Chinese crude oil futures market.