A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast

Wu, Junhao; Dong, Jinghan; Wang, Zhaocai; Hu, Yuan; Dou, Wanting

doi:10.1016/j.resourpol.2023.103602

Cited by 27 publications

(5 citation statements)

References 53 publications

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“…Most machine learning methods using oil price data for iterative learning and training can accurately portray the nonlinearity of time series, leading to enhanced prediction accuracy. However, these methods also encounter challenges, such as reduced interpretability of the prediction results, a tendency to fall into local minima, parameter sensitivity, and overfitting [7,30].…”

Section: Literature Reviewmentioning

confidence: 99%

“…(2) For each component obtained after decomposition, studies typically explore and develop diverse machine learning methods for prediction. Recently, some studies have also explored quadratic decomposition for highly complex components or residual terms, followed by modeling using machine learning techniques to enhance prediction accuracy [5,7,42,43]. However, deep learning approaches and econometric models each have their own strengths and weaknesses.…”

Section: Literature Reviewmentioning

confidence: 99%

“…( 6) Compute the fitness value and update the sparrow positions. (7) Evaluate if the stop criterion is met. If so, quit and print the result; otherwise, repeat steps ( 2)-( 6).…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

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Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm

Lu,

Huang

2024

Entropy

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Precisely forecasting the price of crude oil is challenging due to its fundamental properties of nonlinearity, volatility, and stochasticity. This paper introduces a novel hybrid model, namely, the KV-MFSCBA-G model, within the decomposition–integration paradigm. It combines the mixed-frequency convolutional neural network–bidirectional long short-term memory network-attention mechanism (MFCBA) and generalized autoregressive conditional heteroskedasticity (GARCH) models. The MFCBA and GARCH models are employed to respectively forecast the low-frequency and high-frequency components decomposed through variational mode decomposition optimized by Kullback–Leibler divergence (KL-VMD). The classification of these components is performed using the fuzzy entropy (FE) algorithm. Therefore, this model can fully exploit the advantages of deep learning networks in fitting nonlinearities and traditional econometric models in capturing volatilities. Furthermore, the intelligent optimization algorithm and the low-frequency economic variable are introduced to improve forecasting performance. Specifically, the sparrow search algorithm (SSA) is employed to determine the optimal parameter combination of the MFCBA model, which is incorporated with monthly global economic conditions (GECON) data. The empirical findings of West Texas Intermediate (WTI) and Brent crude oil indicate that the proposed approach outperforms other models in evaluation indicators and statistical tests and has good robustness. This model can assist investors and market regulators in making decisions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm

Lu,

Huang

2024

Entropy

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“…A confidence interval adjustment coefficient was introduced to derive the prediction result. Wu et al (2023) used the Long-short term memory network to fit the data after the variational modal decomposition. Interval prediction results for West Texas Intermediate (WTI) and Brent crude oil futures prices are obtained using gate recurrent unit and kernel density estimation.…”

Section: Crude Oil Futures Prices Prediction 91mentioning

confidence: 99%

“…A confidence interval adjustment coefficient was introduced to derive the prediction result. Wu et al. (2023) used the Long-short term memory network to fit the data after the variational modal decomposition.…”

Section: Introductionmentioning

confidence: 99%

A matrixed nonlinear exponential grey Bernoulli model for interval number prediction of crude oil futures prices

Cang,

Zeng,

Yan

2023

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PurposeThe effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.Design/methodology/approachFirst, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.FindingsThe predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.Originality/valueCrude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.

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Deep learning models for multi-step prediction of water levels incorporating meteorological variables and historical data

Chen,

Wang,

Jiang

et al. 2024

Stoch Environ Res Risk Assess

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A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast

Cited by 27 publications

References 53 publications

Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm

Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm

A matrixed nonlinear exponential grey Bernoulli model for interval number prediction of crude oil futures prices

Deep learning models for multi-step prediction of water levels incorporating meteorological variables and historical data

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