Crude Oil Price Prediction Research Articles

Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator

The economic model derived from the supply and demand of crude oil prices is a significant component that measures economic development and sustainability. Therefore, it is essential to mitigate crude oil price volatility risks by establishing models that will effectively predict prices. A promising approach is the application of long short-term memory artificial neural networks for time-series forecasting. However, their ability to tackle complex time series is limited. Therefore, a decomposition-forecasting approach is taken. Furthermore, machine learning model accuracy is highly dependent on hyper-parameter settings. Therefore, in this paper, a modified version of the salp swarm algorithm is tasked with determining satisfying parameters of the long short-term memory model to improve the performance and accuracy of the prediction algorithm. The proposed approach is validated on real-world West Texas Intermediate (WTI) crude oil price data throughout two types of experiments, one with the original time series and one with the decomposed series after applying variation mode decomposition. In both cases, models were adjusted to conduct one, three, and five-steps ahead predictions. According to the findings of comparative analysis with contemporary metaheuristics, it was concluded that the proposed hybrid approach is promising for crude oil price forecasting, outscoring all competitors.

Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm

Stable and accurate prediction of crude oil prices is critical to national security, economic development, and even international relations, against the background of the COVID-19 and Russia–Ukraine war. Therefore, a memory-based hybrid forecasting system is established for point prediction and interval prediction of crude oil prices in this research, which more thoroughly separates the noise in the raw data and achieves superior prediction performance. There are five main steps in the proposed model: decomposing the raw data into intrinsic mode functions (IMFs) through our new data preprocessing technique complete ensemble extreme-point symmetric mode decomposition with adaptive noise (CEESMDAN), ensemble of IMFs based on memory features, prediction of reconstructed components, error correction via grey wolf optimizer (GWO) for final point prediction, obtaining interval prediction by multi-objective grey wolf optimizer (MOGWO). The empirical results prove that the proposed model has excellent accuracy, robustness, and generalization in both point prediction and interval prediction, compared with various baseline models.

Predicting Crude Oil Price in Nigeria with Machine Learning Models

Background: The rise in crude oil prices yields serious consequences for both oil-producing and non-oil-producing countries. The increase in global commodity prices contributes to the financial income and foreign exchange reserves of oil-exporting countries. However, countries such as Nigeria that sell crude oil and purchase refined fuel confront more complicated situations. To this end, there exists a need to obtain a robust prediction model for the crude oil price of Nigeria. Objective: This study is to determine the best model among the machine-learning time series models considered to predict crude oil prices in Nigeria. Methods: The alternative models were the auto-regressive integrated moving average model, Naive Bayes, Holtwinter trend model, exponential smoothing model, and neural network autoregressive (NNETAR) model. The prediction criteria adopted for model screening were the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Daily crude oil prices in dollars obtained from the Central Bank of Nigeria were used for analysis spanning from October 1, 2009 to March 22, 2022 with 2836 data points. Results: The NNETAR model showed the minimum RMSE, MAE, and MAPE for cross-validation sets considered. Conclusion: The NNETAR model was recommended for the prediction of crude oil prices in Nigeria.

Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning

Prediction methods have become a hot topic in intelligent decision making. Most of the existing prediction methods focus on the prediction accuracy and stability. As a second choice, accurate interval prediction can provide a relatively reliable reference in the sense of probability and provide help for assisting decision management. Therefore, we propose a novel interval prediction approach. Firstly, the decomposition method based on ensemble empirical mode decomposition (EEMD) is utilized to alleviate the complexity of the original time series, thereby generating a series of relatively smooth subseries. Secondly, a three-way clustering (TWC) algorithm is established by integrating sample entropy into probabilistic rough set, enriching the three-way clustering theory from the perspective of entropy. Thirdly, aiming at determining the optimal input dimensions of different neural networks, the feature selection technique based on phase space reconstruction (PSR) is constructed. Furthermore, an interval prediction system based on TWC is proposed to provide a new data-driven prediction method. Finally, the proposed approach is applied to predict the interval price of crude oil. On the one hand, the practicability of the constructed prediction approach is verified; on the other hand, it provides a new theoretical method for interval prediction of crude oil price. The experiment results show the proposed prediction approach can assist the decision-makers to make scientific and reasonable decisions.

Predicting the changes in the WTI crude oil price dynamics using machine learning models

This study aims to use a monthly dataset from 1991 to 2021 to predict West Texas Intermediate (WTI) oil price dynamics using U.S. macroeconomic and financial factors, as well as a global crisis and crashes. We used advanced machine learning models such as Logistic Regression, Decision Tree, Random Forest, AdaBoost, and XgBoost in this study. According to the results, the XgBoost and Random Forest models outperform traditional models. We also used DeLong statistical test procedures to accurately compare machine learning models' performance. In addition, the study used SHAP - SHapley Additive exPlanations values to support model evaluation and interpretability. This new outline highlights the critical features of the WTI crude oil price prediction and provides appropriate model explanations by utilizing the practical SHAP values. The empirical findings showed that machine learning models could successfully and accurately predict the trend of WTI crude oil price changes. Our findings are important for policymakers, companies, and investors, as well as long-term energy-based economic development.

Prediction of crude oil prices in COVID-19 outbreak using real data

The world has been undergoing a global economic recession for almost two years because of the health crisis stemming from the outbreak and its effects have still continued so far. Especially, COVID-19 reduced consumer spending due to social isolation, lockdown and travel restrictions in 2020. As a result of this, with social and economic life coming to a standstill, oil prices plummeted. With the ongoing uncertainty concerning the COVID-19 pandemic, it has been of great importance for all economic agents to predict crude oil prices. The objective of this paper is to improve a model in order to make more accurate predictions for crude oil price movements. The performance of this model is assessed in terms of some significant criteria comparing our model with its counterparts as well as artificial neural networks (ANNs) and support vector machine (SVM) methods. As for these criteria, root mean square error (RMSE) and mean absolute error (MAE) results show that this model outperforms other models in forecasting crude oil prices. Further, the simulation results for 2021 show that the daily crude oil price forecasts are almost close to the real oil prices.Oil price forecasting has become more and more important for economic agents in COVID-19 period. A consistent model is required to cope with the movements in crude oil prices. A novel method combining fuzzy time series and the greatest integer function is developed. The results show that our model outperforms other counterparts or ANN and SVM methods. We capture non-linearity and volatility in crude oil prices.

Forecasting Crude Oil Prices with a WT-FNN Model

In order to improve the accuracy of forecasting crude oil prices, a new crude oil price forecasting method is introduced in the paper that is a combination of the FNN model and the stochastic time effective function—namely, the WT-FNN model. The FNN model keeps track of the historical values of crude oil prices and predicts future crude oil prices, and the stochastic time effective function gives greater weight to recent information and smaller weight to old information, thus making the prediction of crude oil prices more reasonable. We selected the daily data of Brent crude oil prices from 4 January 2000 to 30 September 2021 as research objects and then used the WT-FNN model to train and predict the research objects. By comparing it to the benchmark model, we found that the predictive effect of the WT-FNN model was better than the FNN model and the no-change model. The results also passed a robustness test.

Improved CEEMDAN, GA, and SVR Model for Oil Price Forecasting.

Accurate prediction of crude oil prices (COPs) is a challenge for academia and industry. Therefore, the present research developed a new CEEMDAN-GA-SVR hybrid model to predict COPs, incorporating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a genetic algorithm (GA), and support vector regression machine (SVR). First, our team utilized CEEMDAN to realize the decomposition of a raw series of COPs into a group of comparatively simpler subseries. Second, SVR was utilized to predict values for every decomposed subseries separately. Owing to the intricate parametric settings of SVR, GA was employed to achieve the parametric optimisation of SVR during forecast. Then, our team assembled the forecasted values of the entire subseries as the forecasted values of the CEEMDAN-GA-SVR model. After a series of experiments and comparison of the results, we discovered that the CEEMDAN-GA-SVR model remarkably outperformed single and ensemble benchmark models, as displayed by a case study finished based on a time series of weekly Brent COPs.

A Blending Ensemble Learning Model for Crude Oil Price Prediction

A Blending Ensemble Learning Model for Crude Oil Price Prediction

Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization

Estimating the price of crude oil, which is seen as an important resource for economic development and stability in the world, is a topic of great interest by policy makers and market participants. However, the chaotic and nonlinear characteristics of crude oil time series (COTS) make it difficult to estimate crude oil prices with high accuracy. To overcome these challenges, a new crude oil price prediction model is proposed in this study, which includes the long short-term memory (LSTM), technical indicators such as trend, volatility and momentum, and the chaotic Henry gas solubility optimization (CHGSO) technique. In the proposed model, features based on trend, momentum and volatility technical indicators are utilized. The features are obtained by using the trend indicators such as exponential moving average (EMA), simple moving average (SMA) and Kaufman's adaptive moving average (KAMA), the momentum indicators such as commodity channel index (CCI), rate of change (ROC) and relative strength index (RSI), and the volatility indicators such as average true range (ATR), volatility ratio (VR) and highest high-lowest low (HHLL). These indicators are obtained separately for the West Texas Intermediate (WTI) and Brent COTS. Especially, including the volatility indicator in the model is important in terms of the robustness of the proposed model. The features based on EMA, SMA, KAMA indicators are composed by changing the period values between 3 and 10, the features based on ROC indicator is created by changing the period values between 5 and 12, and the features based on CCI, RSI, ATR, VR and HHLL indicators are formed by changing the period values between 5 and 20. The features are selected by CHGSO algorithm based on the logistic chaotic map, which is successful in avoiding local optima and balancing exploitation and exploration in the search space. Both Theil's U and the mean absolute percentage error (MAPE) values are utilized in the optimization algorithm as the objective function. The results show that the proposed prediction model copes with the chaoticity and nonlinear dynamics of both WTI and Brent COTS.

Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance

Crude oil plays an important role in the world economy and contributes to more than one third of energy consumption worldwide. The better forecasting of its fluctuating price is crucial for policymaking. Although various methods had been previously applied for forecasting crude oil price which including both statistical models and Artificial Neural Network models but we find that there is no single paper that compares the AdaBoost-LSTM and AdaBoost-GRU models for improving forecasting performance. We proposed AdaBoost-GRU in which the GRU model was built, put inside sklearn wrapped and finally boost by AdaBoost Regressor. The predictive power of the proposed model was compared with AdaBoost-LSTM using daily Crude oil prices from October 23, 2009, to June 23, 2021, and single LSTM and single GRU were used as the benchmarking models. Forecasting performances were measured using five different metrics namely; the mean absolute error (MAE), the root mean squared error (RMSE), the Scatter Index (SI), the mean absolute percentage error (MAPE), and the weighted mean absolute percentage error (WMAPE). We have demonstrated that the AdaBoost-LSTM and the AdaBoost-GRU models outperform the benchmarking models as expected, and the empirical results show that the AdaBoost-GRU is superior to all models studied in this research.

Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm

The prediction of crude oil prices has important research significance. The paper contributes to the literature of hybrid models for forecasting crude oil prices. We apply ensemble empirical mode decomposition (EEMD) to decompose the residual term (RES), which contains complex information after variational mode decomposition (VMD), further combining with a kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) to construct the VMD-RES.-EEMD-PSO-KELM model. In order to verify the validity of the model, this paper conducts empirical analyses of Brent crude oil and West Texas Intermediate (WTI) crude oil. The empirical results show that the prediction model proposed in this paper improves the prediction accuracy of crude oil prices.

Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index

Crude oil is one of the most powerful types of energy and the fluctuation of its price influences the global economy. Therefore, building a scientific model to accurately predict the price of crude oil is significant for investors, governments and researchers. However, the nonlinearity and nonstationarity of crude oil prices make it a challenging task for forecasting time series accurately. To handle the issue, this paper proposed a novel forecasting approach for crude oil prices that combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Long Short-Term Memory (LSTM) with attention mechanism and addition, following the well-known “decomposition and ensemble” framework. In addition, a news sentiment index based on Chinese crude oil news texts was constructed and added to the prediction of crude oil prices. And we made full use of attention mechanism to better integrate price series and sentiment series according to the characteristics of each component. To validate the performance of the proposed CEEMDAN-LSTM_att-ADD, we selected the Mean Absolute Percent Error (MAPE), the Root Mean Squared Error (RMSE) and the Diebold-Mariano (DM) statistic as evaluation criterias. Abundant experiments were conducted on West Texas Intermediate (WTI) spot crude oil prices. The proposed approach outperformed several state-of-the-art methods for forecasting crude oil prices, which proved the effectiveness of the CEEMDAN-LSTM_att-ADD with the news sentiment index.

Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine.

In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.

A new crude oil price forecasting model based on variational mode decomposition

Crude oil price prediction helps to get a better understanding of the global economic situation. Recently, variational mode decomposition (VMD) is introduced into the field of crude oil price forecasting. However, there is a lack of general selection rule for VMD-parameter and the widely adopted one-time decomposition strategy seems not suitable for practical application. Thus, an improved signal-energy based (ISE) rule is proposed as an improvement of the existing signal-energy based (SE) rule for the VMD-parameter selection. The moving-window strategy is put forward as a supplement for the decomposition strategy. Finally, a prediction model (VMD-LSTM-MW model) is built by combining the VMD, the long short-term memory (LSTM) network, and the moving-window strategy. The effectiveness of the ISE rule, the validity of the moving-window strategy, and the superiority of the VMD-LSTM-MW model are demonstrated by conducting monthly and daily crude oil price prediction experiments.

Effective crude oil price forecasting using new text-based and big-data-driven model

This study proposes a novel data-driven crude oil price prediction methodology using Google Trends and online media text mining. Convolutional neural network (CNN) is used to automatically extract text features from online crude oil news to illustrate the explanatory power of text features for crude oil price prediction. Specifically, our findings contribute to the methodological and theoretical insights for information processing, in that variational mode decomposition is used to construct useful time series indicators based on the outputs of CNN. Experimental results imply that the proposed text-based and online-big-data-based forecasting methods outperform other techniques. A total of 4837 and 3883 news headlines are collected in two cases, respectively. The mean absolute percentage error of the proposed model is 0.0571 and 0.0459 for crude oil price forecasting of two cases, respectively. Therefore, the complementary relationship between news headlines and Google Trends is beneficial in conducting considerably accurate crude oil price forecasting.

A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price

Accurately crude oil price prediction remains challenging so far. Despite the abundant research achievements of crude oil price prediction, most of them emphasize the linear and deterministic modeling, which cannot adequately capture the complex nonlinear characteristics and uncertainties involved, thus impeding further developments in the field. In this article, a novel learning system with the aim of obtaining the deterministic and probabilistic predictions is presented to model the nonlinear dynamics in crude oil price, composed by the modules of recurrence analysis, outlier detection, data preprocessing, feature selection, predictive modeling based on deep learning, and system evaluation. In particular, the temporal convolution is developed to perform feature selection, thus improving the generalization of the system. Additionally, the extensions, including the predictive performance test evaluation, convergence investigation, and sensitivity analysis, are carried out. The experimental simulations show that the proposed system can yield the deterministic and probabilistic predictions with higher accuracy and feasibility compared with the benchmarks considered, further indicating its effectiveness.

Deep multi-hybrid forecasting system with random EWT extraction and variational learning rate algorithm for crude oil futures

Machine learning algorithms provide feasibility for crude oil price prediction. In this paper, a novel multi-hybrid predictive neural network model is proposed based on complex deep learning algorithm, which integrates empirical wavelet transform, random inheritance formula error correction algorithm, deep bidirectional LSTM neural network and Elman recurrent neural network with variational learning rate. The prediction model is selected according to the sequence frequency after EWT feature extraction, and the prediction results are obtained by separately predicting and reintegrating. On the basis of individual model, the structure of deep bidirectional training, random inheritance formula and variational learning rate are proposed, which further ameliorate the performance of the model and achieve more effective data information capture. Simultaneously, the examination of variational learning rate provides us with a feasible parameter selection. The proposed model achieves high-precision prediction of crude oil futures price, and stands out in the multi-model comparison analysis and q-DSCID synchronous evaluation, with superior prediction accuracy.

Crude oil price prediction based on LSTM network and GM (1,1) model

PurposeAccording to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.Design/methodology/approachFirst, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.FindingsThe model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.Originality/valueThis paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks

Accurate prediction of crude oil prices is meaningful for reducing firm risks, stabilizing commodity prices and maintaining national financial security. Wrong crude oil price forecasts can bring huge losses to governments, enterprises, investors and even cause economic and social instability. Many classic econometrics and computational approaches show good performance for the ordinary time series prediction tasks, but not satisfactory in crude oil price predictions. They ignore the characteristics of non-linearity and non-stationarity of crude oil prices data, which hinder an accurate prediction and eventually lead to poor accuracy or the wrong result. Empirical mode decomposition (EMD) and ensemble EMD (EEMD) solve the problems of non-stationary time series forecasting, but they also generate new problems of mode mixing and reconstruction errors. We propose a hybrid method that is combination of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer gated recurrent unit (ML-GRU) neural network to solve the abovementioned issues. This not only deals with the issue of mode mixing effectively, but also makes the reconstruction error of data close to zero. Multi-layer GRU has an excellent ability of nonlinear data-fitting. The experimental results of real WTI crude oil dataset show that the proposed approach perform better in crude oil prices forecasts than some state-of-the-art models.