Crude Oil Price Forecasting Research Articles

Oil Price Forecasting Using a Time-Varying Approach

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

Crude oil price analysis and forecasting: A perspective of “new triangle”

Abstract In this paper, the new structural characteristics and core influencing factors of the crude oil prices are summarized based on previous representative research results. Firstly, a newly dynamic Bayesian structural time series model (DBSTS) is developed to investigate the oil prices. In particular, Google trend is introduced as an indicator to reflect the impact of search data on the oil price. Secondly, the spike and slab method is employed to select core influence factors. Finally, the Bayesian model average (BMA) is utilized to predict the oil price. Experimental results confirm that the supply and demand of global crude oil and the financial market are still the main factors affecting the oil price. Furthermore, Google trend can reflect the changes in the crude oil price to a certain extent. Moreover, the impact of shale oil production on the oil price is gradually increasing, yet remains relatively small. In addition, the DBSTS model can identify turning points in historical data (such as the 2008 financial crisis). Finally, the findings suggest the DBSTS model has good predictive capabilities in short-term prediction, making it suitable for analyzing the crude oil prices.

Application of ARIMA-GARCH Model for Prediction of Indonesian Crude Oil Prices

Crude oil is one of the most important energy commodities for various sectors. Changes in crude oil prices will have an impact on oil-related sectors, and even on the stock price index. Therefore, the prediction of crude oil prices needs to be done to avoid the future prices of these non-renewable natural resources to increase dramatically. In this paper, the prediction of crude oil prices is carried out using the Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) models. The data used for forecasting are Indonesian Crude Price (ICP) crude oil data for the period January 2005 to November 2012. The results show that the data analyzed follows the ARIMA(1,2,1)-GARCH(0,3) model, and the crude oil price forecast for December 2012 is 105.5528 USD per barrel. The prediction results of crude oil prices are expected to be important information for all sectors related to crude oil.

Multidisciplinary study of the problems of big data technologies in the oil and gas industry

This paper is devoted to the analysis of the multidisciplinary problems of the big data technology in the oil and gas industry. Application capabilities of big data technologies in issues such as reducing the exploitation risks, crude oil price forecasting, optimal management of the oil wells, health and safety ensuring in an organisation, overcoming environmental problems and so on are investigated. [Received: April 18, 2017; Accepted: February 2, 2018]

Multidisciplinary study of the problems of big data technologies in the oil and gas industry

This paper is devoted to the analysis of the multidisciplinary problems of the big data technology in the oil and gas industry. Application capabilities of big data technologies in issues such as reducing the exploitation risks, crude oil price forecasting, optimal management of the oil wells, health and safety ensuring in an organisation, overcoming environmental problems and so on are investigated. [Received: April 18, 2017; Accepted: February 2, 2018]

A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

Crude oil price forecasting is an essential component of sustainable development of many countries as crude oil is an unavoidable product that exists on earth. In this paper, a model based on a hidden Markov model and Markov model for crude oil price forecasting was developed, and their relative performance was compared. Path analysis of Structural Equation Modelling was employed to model the effects of forecasted prices and the actual crude oil price to get the most accurate forecast. The key variables used to develop the models were monthly crude oil prices s from PETRONAS Malaysia. It was found that the hidden Markov model was more accurate than the Markov model in forecasting the crude oil price. The findings of this study show that the hidden Markov model is a potentially promising method of crude oil price forecasting that merit further study.

Predicting Oil Prices: An Analysis of Oil Price Volatility Cycle and Financial Markets

ABSTRACT Given the importance of crude oil prices in the world economy, accurate price prediction has drawn extensive attention. Nevertheless, because of the complexity of the crude oil market, most traditional forecasting algorithms fail to meet the accuracy requirements. To achieve higher precision, this paper proposes a novel hybrid model for crude oil price forecasting by combining a Hodrick-Prescott filter with X12 methods and adjusting the order used. Application of our model on both West Texas Intermediate and Brent oil prices forecasting demonstrates its accuracy. The results of various forecasting performance evaluation criteria indicate that the model has stronger stability and better accuracy. The mechanism of seasonal and periodic factors is also analyzed, which provides remarkable references to other time-series predictions. Establishing two different types of predictive models that combine multiple knowledge effectively has obvious advantages over other models and provides more reliable cutting-edge information for designing a Chinese energy development strategy.

Type 2 Fuzzy Inference-Based Time Series Model

Fuzzy techniques have been suggested as useful method for forecasting performance. However, its dependency on experts’ knowledge causes difficulties in information extraction and data collection. Therefore, to overcome the difficulties, this research proposed a new type 2 fuzzy time series (T2FTS) forecasting model. The T2FTS model was used to exploit more information in time series forecasting. The concepts of sliding window method (SWM) and fuzzy rule-based systems (FRBS) were incorporated in the utilization of T2FTS to obtain forecasting values. A sliding window method was proposed to find a proper and systematic measurement for predicting the number of class intervals. Furthermore, the weighted subsethood-based algorithm was applied in developing fuzzy IF–THEN rules, where it was later used to perform forecasting. This approach provides inferences based on how people think and make judgments. In this research, the data sets from previous studies of crude palm oil prices were used to further analyze and validate the proposed model. With suitable class intervals and fuzzy rules generated, the forecasting values obtained were more precise and closer to the actual values. The findings of this paper proved that the proposed forecasting method could be used as an alternative for improved forecasting of sustainable crude palm oil prices.

An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain Optimization and Refrac Candidate Identification

Summary The unsteady recovery of oil and gas prices in early 2017 led to an increase in drilling and hydraulic–fracturing operations in liquid–rich shale plays in North America. As field–development strategies continue to evolve, refracturing and infill–well drilling must be carefully combined to optimize shale–project profitability. Moreover, operators must bear in mind the undulating natural–gas demands persisting in an oversupplied shale–gas environment. In this paper, we use data–driven approaches to predict successful refracturing candidates and local gas demand for the second–tier optimization of a shale–gas supply–chain network. A strategic–planning (SP) model is developed for optimizing the net present value (NPV) of a case–study shale–gas network in the Marcellus Play. This SP model uses a mixed–integer–nonlinear–programming (MINLP) formulation developed in the General Algebraic Modeling System (GAMS, Release 27.1.0.2019). This model relies directly on input from reservoir simulation, local–gas–demand forecast, water–availability forecast, and natural–gas and West Texas Intermediate (WTI) crude–oil price forecasts. Before reservoir simulation, machine learning (ML) is used to predict successful refracturing candidates, using a feed–forward neural network (NN), random–forest (RF) classifier, and a t–distributed stochastic–neighbor–embedding (t–SNE) visualization technique. Using the obtained results, best–practice field–development strategies are implemented in the area of interest (AOI) using reservoir simulation. Local gas demand is forecasted using a long–short–term–memory (LSTM) recurrent NN (RNN) that uses a multivariate data set created from local and global variables affecting shale–gas demand. A water–management structure is also developed for the optimization framework. Using a 300–well data set (with 17 input features), successful refracturing candidates were proposed according to the joint outcome of an optimal 17/23/128/2 feed–forward NN, a t–SNE plot, and a techno–economic review. After ranking F1 scores, the developed NN outperforms the RF and support–vector–machine (SVM) algorithms for frac/refrac–well classification. The developed 32/256/128/120 LSTM model showed at least a 93% (±1%) prediction performance using three or five input features. The results illustrate the ability of the developed LSTM model to accurately predict local gas demands during periods of high or low gas demand. After SP optimization over a 10–year planning horizon, the economic results indicate an NPV of USD 481.945 million, using the proposed physics–data–driven–based approach. An NPV of USD 611.22 million is obtained when no ML was used. The results reveal that the application of ML to strategic planning can prevent erroneous feedback of project profitability while allowing early–time decision making that maximizes shale–asset NPV.

Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.

Monthly crude oil spot price forecasting using variational mode decomposition

Crude oil is one of the most important trade commodities in the world and its price fluctuation has significant effects on global economic activities. In this paper, we proposed hybrid models for monthly crude oil price forecasting using variational mode decomposition and artificial intelligence (AI) techniques (in this paper, support vector machine optimized by genetic algorithm (GASVM) and back propagation neural network optimized by genetic algorithm (GABP) are employed for analyzing). In addition, influencing factors of the long-term crude oil price such as the global crude oil production as well as economic activity (Dow Jones Industrial Index is considered in this paper) are investigated and considered on the crude oil price forecasting. Empirical forecasting results of monthly West Texas Intermediate (WTI) and Brent crude oil spot prices validate that the hybrid VMD-based models are superior to previously popular EEMD-based models and single models in terms of both level and directional prediction accuracies as well as the DM test results. In addition, the VMD-AI based models which consider influencing factors of the long-term crude oil price variation perform better than that without considering influencing factors of the long-term crude oil price variation in terms of MAPE and RMSE. All of which confirm that the newly proposed VMD-AI based models are promising tools for crude oil price analysis and forecasting.

A hybrid Bayesian-network proposition for forecasting the crude oil price

This paper proposes a hybrid Bayesian Network (BN) method for short-term forecasting of crude oil prices. The method performed is a hybrid, based on both the aspects of classification of influencing factors as well as the regression of the out-of-sample values. For the sake of performance comparison, several other hybrid methods have also been devised using the methods of Markov Chain Monte Carlo (MCMC), Random Forest (RF), Support Vector Machine (SVM), neural networks (NNET) and generalized autoregressive conditional heteroskedasticity (GARCH). The hybrid methodology is primarily reliant upon constructing the crude oil price forecast from the summation of its Intrinsic Mode Functions (IMF) and its residue, extracted by an Empirical Mode Decomposition (EMD) of the original crude price signal. The Volatility Index (VIX) as well as the Implied Oil Volatility Index (OVX) has been considered among the influencing parameters of the crude price forecast. The final set of influencing parameters were selected as the whole set of significant contributors detected by the methods of Bayesian Network, Quantile Regression with Lasso penalty (QRL), Bayesian Lasso (BLasso) and the Bayesian Ridge Regression (BRR). The performance of the proposed hybrid-BN method is reported for the three crude price benchmarks: West Texas Intermediate, Brent Crude and the OPEC Reference Basket.

Modes decomposition method in fusion with robust random vector functional link network for crude oil price forecasting

This paper proposes a decomposition based method in fusion with the non-iterative approach for crude oil price forecasting. In this approach, the robust random vector functional link network (RVFLN), a non-iterative approach in fusion with the most efficient decomposition technique called variational mode decomposition (VMD) is proposed which is executed with two links — fixed assigned random weights and direct link from input to output, and the iterative learning process is not involved in its functioning which makes it faster in execution as compared to many existing techniques proposed for forecasting. The fusion of VMD and robust RVFLN called VMD-RVFLN is implemented for crude oil price forecasting where the crude oil price series is decomposed using VMD into a linear smoother series by extracting useful information and the decomposed modes pass through the robust RVFLN model which produces the final forecasting values. The analysis performed in the study approves its efficiency and reports improvement in forecasting accuracy and execution time as compared to some of the traditional iterative techniques like BPNN (back propagation neural network), ARIMA (auto-regressive integrated moving average), LSSVR (least squares support vector regression), ANFIS (adaptive neuro-fuzzy inference system), IT2FNN (interval type-2 fuzzy neural network) and RNN (recurrent neural network), etc. However, both ELM and RVFLN without modes decomposition fusion exhibit less execution time at the cost of reduction in prediction accuracy.

A novel decomposition-ensemble approach to crude oil price forecasting with evolution clustering and combined model

In order to deal with non-stationary and chaotic series, a hybrid forecasting approach is proposed in this study, which integrates ensemble empirical mode decomposition (EEMD) and optimal combined forecasting model (CFM). The proposed approach introduces a new intrinsic mode functions (IMFs) reconstruction method by using evolutionary clustering algorithm, and utilizes optimal combined model to forecast sub-series. Firstly, the EEMD technique is employed to sift the IMFs and a residue. Secondly, the comprehensive contribution index (CCI) of each IMF is calculated and IMFs are further reconstructed by evolutionary clustering algorithm according to CCI of each IMF. Then, a new sub-series called virtual intrinsic mode functions (VIMFs) is defined and obtained. Thirdly, the optimal combined forecasting model is developed to forecast the VIMFs and residues. In the end, the final forecasting results are obtained by summing the forecasts of VIMFs and residue. For illustration and comparison, the West Texas Intermediate (WTI) crude oil price data are shown as a numerical example. The research results show that the proposed approach outperforms benchmark models in terms of some forecasting assessment measures. Therefore, the proposed hybrid approach can be utilized as an effective model for the forecasting of crude oil price.

Improving forecasting accuracy of crude oil prices using decomposition ensemble model with reconstruction of IMFs based on ARIMA model

The accuracy of crude oil price forecasting is more important especially for economic development and is considered a lifeblood of the industry. Hence, in this paper, a decomposition-ensemble model with the reconstruction of intrinsic mode functions (IMFs) is proposed for forecasting the crude oil prices based on the well-known autoregressive moving average (ARIMA) model. Essentially, the reconstruction of IMFs enhanced the forecasting accuracy of the existing decomposition ensemble models. The proposed methodology works in four steps: decomposition of the complex data into several IMFs using EEMD, reconstruction of IMFs based on order of ARIMA model, prediction of every reconstructed IMF, and finally ensemble the prediction of every IMF for the final output. A case study is carried out using two crude oil prices time series (i.e. Brent and West Texas Intermediate (WTI)). The empirical results exhibited that the reconstruction of IMFs based on order of ARIMA model was adequate and provided the best forecast. To check the correctness, robustness and generalizability simulations were also carried out.

Crude Oil Prices Forecasting: Time Series vs. SVR Models

This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 - 2017 to test the forecasting accuracy by comparing time series models such as simple exponential smoothing (SES), moving average (MA), and autoregressive integrated moving average (ARIMA) against machine learning support vector regression (SVR) models. The main purpose of this research is to determine which model provides the best forecasting results for crude oil prices in light of the importance of crude oil price forecasting and its implications to the economy. While SVR is often considered the best forecasting model in the main stream literature, this research investigates its computational insights in terms of parameter selections and overfitting potential, in addition to exploring forecasting accuracy and model comparison. The results of this research can be generalized to forecast other business and economic time series data such as stock market prices, product sales, and government statistics.

Text-based crude oil price forecasting: A deep learning approach

This study proposes a new, novel crude oil price forecasting method based on online media text mining, with the aim of capturing the more immediate market antecedents of price fluctuations. Specifically, this is an early attempt to apply deep learning techniques to crude oil forecasting, and to extract hidden patterns within online news media using a convolutional neural network (CNN). While the news-text sentiment features and the features extracted by the CNN model reveal significant relationships with the price change, they need to be grouped according to their topics in the price forecasting in order to obtain a greater forecasting accuracy. This study further proposes a feature grouping method based on the Latent Dirichlet Allocation (LDA) topic model for distinguishing effects from various online news topics. Optimized input variable combination is constructed using lag order selection and feature selection methods. Our empirical results suggest that the proposed topic-sentiment synthesis forecasting models perform better than the older benchmark models. In addition, text features and financial features are shown to be complementary in producing more accurate crude oil price forecasts.

Improved EEMD-based crude oil price forecasting using LSTM networks

Considering the actual demand of crude oil price forecasting, a novel model based on ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) is proposed. In practical work, the model trained by historical data will be used in later data. Then the forecasting models based on EEMD need re-execute EEMD to update decomposition results of price series after getting new data. In this process, the decomposition results of same period will not stay entirely identical, and even the number of decomposition results could change Unfortunately, in this case the traditional decomposition-ensemble models trained by historical data break down. To overcome this disadvantage, a method to select same number of proper inputs in different situations of decomposition results is developed. And for extracting feature from selected components more adequately, LSTM is introduced as forecasting method to predict price movement directly. For illustration and verification purposes, the proposed model is used to predict the crude oil spot price of West Texas Intermediate (WTI). Empirical results demonstrate that the proposed model still work well when the number of decomposition results varies, thus is promising for forecasting crude oil price.

Are published oil price forecasts efficient?

AbstractOil prices are crucial for a wide area of economic decisions ranging from households over business to economic policymaking. Therefore, oil price projections are a crucial input to economic models and to economic activity forecasts. This paper assesses the accuracy and efficiency of crude oil price forecasts published by different organisations, think tanks and companies. Since the sequence of published forecasts appears as smooth, the weak efficiency criterion is clearly violated. Even combining forecasts, cannot increase efficiency due to high correlation among various forecasts. This pattern of oil price forecasts can be attributed to combining myopia (use current oil price levels as a basis) with Hotelling‐type exponential growth. Another behavioural explanation in source of inefficiencies is that forecasters prefer to harmonise their forecasts with other forecasters in order to be not an outlier.

A Novel Look Back N Feature Approach towards Prediction of Crude Oil Price

Prediction of crude oil prices in advance can play a significant role in the global economy. Change in crude oil price affect wide range of application for economic and risk projection. Crude oil price forecasting is a challenging task due to its complex nonlinear and chaotic behavior. During the last decade’s researcher have designed many classification algorithm for crude oil prediction. The major challenge for any unsupervised dataset is to define a class label for every feature of its dataset. This paper, propose a new novel technique, look back N feature (LBNF) algorithm to discover class label. Later the classifier support vector machine (SVM) with k-nearest neighbor (k-NN) has been used to classify the current feature vector to predict the crude indices one day, one weak, one month in advance. We have checked our algorithm with standard recent MCX INR Daily and CFD USD Real Time crude oil dataset. To prove the effectiveness of proposed algorithm we have compared it with recent Grey wave forecasting method and the experimental result is found to be better than this method.Â