Forecasting Crude Oil Prices using a Hybrid Model Combining Long Short-Term Memory Neural Networks and Markov Switching Model

Shahbazbegian, Vahid; Hosseininesaz, Hamid; Shafie‐khah, Miadreza; Elmusrati, Mohammed

doi:10.1109/fes57669.2023.10182444

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…The lookback parameter is randomly selected from 3 to 10, while the LSTM unit is chosen from 64 to 256. The density unit and learning rate values are specified within the range of [10,100] and [0.01, 0.01], respectively.…”

Section: A Parameter Settingmentioning

confidence: 99%

“…Furthermore, the performance of LSTM has also been documented in reputable studies on time series [9], [10]. Yang et al [9] employed the LSTM model to forecast short-and long-term production events in shale gas wells.…”

Section: Introductionmentioning

confidence: 99%

“…They use a ten-year daily dataset from NASDAQ and key economic input features. A study by Shahbazbegian et al [10] divided time series into sub-series by the proposed hybrid model, which employs a multifaceted approach to capture distinct characteristics, LSTM is combined with the Markov switching model to forecast volatile and fluctuating sub-series.…”

Section: Introductionmentioning

confidence: 99%

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Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

Yusoff,

Ehsan,

Sharif

et al. 2024

IJACSA

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Forecasting crude oil prices hold significant importance in finance, energy, and economics, given its extensive impact on worldwide markets and socio-economic equilibrium. Using Long Short-Term Memory (LSTM) neural networks has exhibited noteworthy achievements in time series forecasting, specifically in predicting crude oil prices. Nevertheless, LSTM models frequently depend on the manual adjustment of hyperparameters, a task that can be laborious and demanding. This study presents a novel methodology incorporating Particle Swarm Optimization (PSO) into LSTM networks to optimize the network architecture and minimize the error. This study employs historical data on crude oil prices to explore and identify optimal hyperparameters autonomously and embedded with the star and ring topology of PSO to address the local and global search capabilities. The findings demonstrate that LSTM+starPSO is superior to LSTM+ringPSO, previous hybrid LSTM-PSO, conventional LSTM networks, and statistical time series methods in its predictive accuracy. LSTM+starPSO model offers a better RMSE of about +0.16% and +22.82% for WTI and BRENT datasets, respectively. The results indicate that the LSTM model, when enhanced with PSO, demonstrates a better proficiency in capturing the patterns and inherent dynamics data changes of crude oil prices. The proposed model offers a dual benefit by alleviating the need for manual hyperparameter tuning and serving as a valuable resource for stakeholders in the energy and financial industries interested in obtaining dependable insights into fluctuations in crude oil prices.

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Section: A Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

Yusoff,

Ehsan,

Sharif

et al. 2024

IJACSA

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A Combined Framework Based on Feature Selection and Multivariate Mixed-Frequency for Crude Oil Prices Point and Interval Forecasting

Long,

Li,

2023

IEEE Access

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Effective crude oil price forecasting is essential for energy supply stabilization, investment decisions, policy formulation, and economic impact assessment. However, previous studies of crude oil futures price forecasting either consider only a single variable without adequately accounting for the influence of various factors on crude oil prices, or they consider only the same frequency of influences and ignore the importance of mixed frequencies, resulting in forecasts that do not achieve the desired results. To overcome these problems, this paper proposes a new multivariate combinatorial mixed frequency forecasting system to predict the weekly closing price of crude oil futures. The system is divided into four modules: Data denoising, Feature selection, Combined forecasting, and Performance evaluation module. To obtain smooth data, ICEEMDAN is used to denoise the original data. Furthermore, to select appropriate variables and reduce model redundancy, recursive feature elimination is used to select appropriate variables with low frequency. Considering the importance of mixed-frequency data, the mutual information method was used to select appropriate high-frequency variables for modeling the crude oil price forecast model. To overcome the shortcomings of Back Propagation Neural Network, Gate Recurrent Unit, and Radial Basis Function Neural Network models, integrate their advantages, and obtain accurate and stable prediction results, a combined forecasting mechanism based on a Multi-Objective Sparrow Search algorithm was developed to obtain both point and interval forecasting results, and finally, two data sets were selected for empirical analysis. The results show that the mean absolute percentage errors of the point forecast of this model are 1.96% and 1.84%, respectively, about 31% and 15% higher than those of the competing models (mean absolute percentage errors 2.57% and 2.13%, respectively). For interval forecasting, the accumulated width deviation is 0.0037 and 0.002, respectively, about 35% and 25% higher than those of the competing models (accumulated width deviation 0.005 and 0.0025, respectively). Thus, the proposed forecasting framework outperforms all comparative models and can be used effectively for forecasting crude oil prices.INDEX TERMS Crude oil price, feature selection, interval forecasting, multi-objective sparrow search algorithm, multivariate mixed-frequency combinatorial forecasting framework.

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Effect of Hyperparameter Tuning in Long Short-Term Memory for Crude Oil Price Prediction

Yusoff,

Sharif,

Sallehud-din

2024

2024 IEEE 6th Symposium on Computers &Amp;amp; Informatics (ISCI)

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Forecasting Crude Oil Prices using a Hybrid Model Combining Long Short-Term Memory Neural Networks and Markov Switching Model

Cited by 3 publications

References 26 publications

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory

A Combined Framework Based on Feature Selection and Multivariate Mixed-Frequency for Crude Oil Prices Point and Interval Forecasting

Effect of Hyperparameter Tuning in Long Short-Term Memory for Crude Oil Price Prediction

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