A Hybrid Autoregressive Integrated Moving Average-PHGMDH Model to Forecast Crude Oil Price

Sarpong-Streetor, Richard M. N. Y.; Sokkalingam, Rajalingam; Othman, Mahmod; Ching, Dennis Ling Chuan; Sakidin, Hamzah

doi:10.32479/ijeep.7987

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Due to nonlinear features, the classical time series models are not capable of predicting crude oil prices accurately [21]. erefore, inspired by the advantage of MEEMD in this study, a novel approach that integrates MEEMD-GMDH is used for forecasting crude oil price.…”

Section: The Proposed Meemd-gmdh Modelmentioning

confidence: 99%

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A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

Ahmad

Aamir

Khalil

et al. 2021

Mathematical Problems in Engineering

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The accuracy of time series forecasting is more important and can assist organizations to take up-to-date decisions for better planning and management. Several classical econometrics and computational approaches show promising results for the ordinary time series forecasting tasks, but they are not satisfactory in crude oil price forecasting. Ensemble empirical mode decomposition (EEMD) not only resolves the problem of nonlinearity and nonstationarity of time series prediction but also creates some problems (i.e., mood mixing and splitting). In this study, we proposed a new hybrid method that combines the median ensemble empirical mode decomposition and group method of data handling (MEEMD-GMDH) to reduce mood splitting problems and forecast crude oil price. MEEMD is achieved by replacing the mean operator with the median operator during the EEMD process. For testing and validation purposes of the different models, the two-seat stamp benchmarked crude oil price data are used (i.e., Brent and West Texas Intermediate (WTI)). To check the proposed model performance, different evaluation measures are used including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Diebold-Mariano (DM) test. All the forecasting accuracy measures confirmed that our proposed model performs well in crude oil prices forecasting as compared to other hybrid models.

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Section: The Proposed Meemd-gmdh Modelmentioning

confidence: 99%

“…is study also concluded that the decision tree models achieved higher prediction accuracy than benchmark models such as multiple linR and ARIMA. Due to nonlinear features, the classical time series models were not capable of predicting crude oil prices accurately [21,22].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

Ahmad

Aamir

Khalil

et al. 2021

Mathematical Problems in Engineering

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A Hybrid Prediction Model for International Crude Oil Price Based on Variational Mode Decomposition with BiTCN-BiGRU-Attention Deep Learning Techniques

Bi,

Liu,

Yang

2024

Preprint

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Predicting the price and volatility of international crude oil futures is a complex task. This paper presents a novel hybrid prediction model, the VMD-BiTCN-BiGRU-Attention, which integrates variational mode decomposition (VMD) and advanced deep learning techniques to forecast the nonlinear, non-stationary, and time-varying characteristics of crude oil price sequences. Initially, the price sequence is decomposed into multiple modes using VMD, enabling the capture of different frequency components. Each mode is independently predicted using a bidirectional time convolutional network (BiTCN), which captures temporal sequence information and enhances long-term dependencies through dilated convolution. Subsequently, a bidirectional gated recurrent unit (BiGRU) models the temporal dependencies more effectively, while an attention mechanism adjusts the weights of the BiGRU outputs to emphasize critical information. The model’s predictions are optimized with the Adam algorithm. Empirical results demonstrate that the model is adept at forecasting non-stationary and nonlinear international crude oil prices. Furthermore, the Diebold-Mariano (DM) test confirms that this model surpasses 15 other models regarding accuracy and performance, achieving optimal results with key metrics: R² = 0.9953, RMSE = 1.4417, MAE = 0.7973, and MAPE = 1.5213%. These findings underscore its potential for enhancing crude oil price prediction.

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A Hybrid Autoregressive Integrated Moving Average-PHGMDH Model to Forecast Crude Oil Price

Cited by 2 publications

References 11 publications

A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

A Hybrid Prediction Model for International Crude Oil Price Based on Variational Mode Decomposition with BiTCN-BiGRU-Attention Deep Learning Techniques

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