Forecasting of WTI crude oil using combined ANN-Whale optimization algorithm

Sohrabi, Parviz; Dehghani, Hesam; Rafiee, Ramin

doi:10.1080/15567249.2022.2083728

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…Hence, the choice of pre-processing techniques would depend upon the data's characteristics and quality. Typically, benchmark datasets such as ready data do not necessitate extensive pre-processing tasks [4]. However, the most significant challenge is effectively managing substantial quantities, especially the time series data, which requires the development of a more understandable model.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…In 2021 and 2022, Sohrabi et al used Whale algorithm and neural network to optimize crude oil and coal price estimation. (Sohrabi, et al 2022;Sohrabi, et al 2021). In a study by Sharman et al (2021), the variables with the greatest effect on BB, i.e., the stemming length and the specific charge, were identified by using 70 data obtained from a blast in one of the open pit coal mines of India through the genetic algorithm (GA) and multiple regression analysis, and it was found that GA is an efficient tool to predict BB (Sharma et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

Sorabi

Ataei

Jazi

et al. 2023

Preprint

Self Cite

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One of the adverse consequences of the blasting in the mineral extraction process in mines is back-break (BB) so that development of many fractures and cracks at large distances behind the last row of blast pits reduces the safety of the benches and increases operating costs. Since various parameters affect the BB, various techniques have been developed to predict and optimize its values. In this study, 48 blasts were investigated in Gol Gohar Mine No. 1 in the tailings section of the mine to predict BB based on the Whale Optimization Algorithm (WOA), Multiverse Optimizer (MVO), Sine Cosine Algorithm (SCA), and Ant Lion Optimizer (ALO). The parameters of bench height, hole length, burden, spacing, specific charge, the number of blasting rows, hole diameter, stemming, uniaxial compressive strength, joint spacing, and geological strength index (GSI) were evaluated as inputs to the models to predict back-breaks in the blasts. The comparison of the results of four BB prediction models suggested that the MVO-based model with a coefficient of determination (R2) of 0.9802, root-mean-square error (RMSE) of 0.2161, and mean squared error (MSE) of 0.1127 had the highest accuracy and the lowest error. So, it was introduced as the most appropriate model for predicting BB.

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“…The combination prediction method was proposed by Bates and Granger (1969) and has become the focus of research at home and abroad in energy (Barassi & Zhao, 2018; Poncela‐Blanco & Poncela, 2021), environment (Avdeeva et al, 2019; Paulraj et al, 2016), tourism (Fatema et al, 2022; Song & Lee, 2009), and so on and most recently in West Texas Intermediate crude oil (Sohrabi et al, 2022) and COVID‐19 (Taylor & Taylor, 2022). Yager (1988) introduced the ordered weighted averaging (OWA) in combination prediction method for aggregate the exact arguments that lie between the max and the min operators, which has substantially increased the forecast precision.…”

Section: Introductionmentioning

confidence: 99%

The optimal interval combination prediction model based on vectorial angle cosine and a new aggregation operator for social security level prediction

Peng,

Kang,

et al. 2023

Journal of Forecasting

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This paper puts forwards the induced generalized ordered weighted multiple continuous ordered weighted geometric averaging (IGOWMC‐OWGA) operator, which overcomes the deficiency that the ordered weighted geometric averaging (C‐OWGA) operator can only integrate a single interval. Main properties of the proposed operator are investigated. In order to integrate interval data and obtain more accurate prediction results, an optimal model based on IGOWMC‐OWGA operator and vectorial angle cosine is constructed. Finally, the model is applied to an empirical analysis of the moderate measure value of social security level about a certain province in China. The sensitivity and comparisons of results are analyzed which shows that the established model is of good stability and can effectively improve the accuracy of prediction.

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Forecasting of WTI crude oil using combined ANN-Whale optimization algorithm

Cited by 11 publications

References 32 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

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

The optimal interval combination prediction model based on vectorial angle cosine and a new aggregation operator for social security level prediction

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