CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms

Bristone, Makumbonori; Prasad, Rajesh; Abubakar, A.

doi:10.1016/j.petlm.2019.11.009

Cited by 33 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[3][4][5][6]. However, COPs are in uenced by numerous complex factors, both observed and unobserved [7][8][9]. erefore, COP forecast is still a hot spot in the academic literature and in industry.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Improved CEEMDAN, GA, and SVR Model for Oil Price Forecasting

Luo

Cui

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Improved CEEMDAN, GA, and SVR Model for Oil Price Forecasting

Luo

Cui

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…The algorithm parameters are determined based on the general characteristics of the empirical correlations' fluid properties. Figure 2 shows the algorithm's structure consisting of three stages: i) inputs, company and oil field, good initial conditions (pressure, temperature, °API, solubility, and gas specific gravity); ii) selection and input of correlations, is the processing of data that responds to the evaluation of mathematical correlations, used according to the physical properties of the fluid, empirical correlations (Table 2) and the storage of correlation data for further studies; iii) the results are the oilś ratio properties (bubble pressure, volumetric factor, compressibility, viscosity) and gas (volumetric factor, viscosity, compressibility factor) [54,55].…”

Section: Algorithm Designmentioning

confidence: 99%

Computer Application to Estimate PVT Conditions in Oil Wells in the Ecuadorian Amazon

Escandón-Panchana¹,

Morante-Carballo²,

Herrera-Franco³

et al. 2021

MMEP

View full text Add to dashboard Cite

A reservoir behaviour's characterisation is determined by analysing the fluids' physical properties, reported in Pressure, Volume and Temperature (PVT) tests. These tests are performed in the laboratory or are estimated by mathematical correlations with the well's basic properties. The eastern basin of Ecuador is considered a hydrocarbon zone, and the analysis of the physical properties of the fluid from oil wells is essential. The aim is to develop the PVTTESTSYSTEM software to estimate PVT conditions when there are no laboratory tests. The study methodology is based on (i) Compilation of 10 PVT laboratory tests of oil wells in the eastern basin of Ecuador; (ii) Analysis of mathematical correlations; (iii) Development of the PVTTESTSYSTEM software, with the wells' initial conditions' input, selecting the mathematical correlation and estimation of results, based on the relationship of the properties of oil and gas; iv) Comparison of data obtained by laboratory tests and PVTTESTSYSTEM software reports. The software used with a graphical interface presents a registration and login platform and five modules that allow: inserting company and field data, initial oil well data, selecting correlations, calculating PVT properties and generating a graphic report. The results show that the mathematical correlations that estimate PVT properties were systematised, which approximate the laboratory tests' real results. The approximation of the calculated results with the actual results establishes a high confidence level for the PVTTESTSYSTEM software.

show abstract

“…Many researchers have found that oil future price has chaotic features such as nonstationary and nonlinear. ese features usually bring thorny challenges to researchers who aim to forecast oil future price [9]. erefore, employing some related deep learning algorithms or statistical models into oil future price prediction is still a popular and interesting topic in growing literature [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Forecasting Approach by the GA-SVR-GRNN Hybrid Deep Learning Algorithm for Oil Future Prices

Wang

Xia

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

It is hard to forecasting oil future prices accurately, which is affected by some nonlinear, nonstationary, and other chaotic characteristics. Then, a novel GA-SVR-GRNN hybrid deep learning algorithm is put forward for forecasting oil future price. First, a genetic algorithm (GA) is employed for optimizing parameters regarding the support vector regression machine (SVR), and the GA-SVR model is used to forecast oil future price. Further, a generalized regression neural network (GRNN) model is built for the residual series for forecasting. Finally, we obtain the predicted values of the oil future price series forecasted by the GA-SVR-GRNN hybrid deep learning algorithm. According to the simulation, the GA-SVR-GRNN hybrid deep learning algorithm achieves lower MSE, RMSE, MAE, and MAPE relative to the GRNN, GA-SVR, and PSO-SVR models, indicating that the proposed GA-SVR-GRNN hybrid deep learning algorithm can fully reveal the prediction advantages of the GA-SVR and GRNN models in the nonlinear space and is a more accurate and effective method for oil future price forecasting.

show abstract

CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms

Cited by 33 publications

References 23 publications

[Retracted] Improved CEEMDAN, GA, and SVR Model for Oil Price Forecasting

[Retracted] Improved CEEMDAN, GA, and SVR Model for Oil Price Forecasting

Computer Application to Estimate PVT Conditions in Oil Wells in the Ecuadorian Amazon

A Novel Forecasting Approach by the GA-SVR-GRNN Hybrid Deep Learning Algorithm for Oil Future Prices

Contact Info

Product

Resources

About