联合ANN-Whale优化算法预测WTI原油

IF 3.1 4区工程技术 Q3 ENERGY & FUELS

Energy Sources Part B-Economics Planning and Policy Pub Date : 2022-07-12 DOI:10.1080/15567249.2022.2083728

Parviz Sohrabi, Hesam Dehghani, Ramin Rafie

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引用次数: 5

摘要

摘要:本研究利用带有鲸鱼优化算法(WOA)的人工神经网络(ANN)预测西德克萨斯中质原油(WTI)价格。在模型的实现中，我们将黄金价格、煤炭价格、天然气价格、美元对欧元汇率、美元对人民币汇率这五个参数作为组合模型的输入。通过对人工神经网络算法和基础人工神经网络算法的比较，发现了人工神经网络- woa算法预测WTI原油未来价格的能力。与人工神经网络相比，ANN- woa模型将WTI价格预测准确率提高了22%。与R2 = 0.75的ANN方法相比，ANN- woa方法的R2 = 0.93能较好地减小模型误差。根据组合模型的输入参数对WTI原油价格预测的显著影响，因此，在预测价格或其他变量的研究中，高度相关的变量可以显著提高预测的准确性。

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

ABSTRACT The current study predicts West Texas Intermediate (WTI) petroleum prices using an artificial neural network (ANN) with a whale optimization algorithm (WOA). In implementing the model, five parameters, including gold price, coal price, natural gas price, Dollar-Euro exchange rate, and Dollar-Yuan exchange rate, have been used as input to the combined model. The intelligent and basic ANN algorithm results compared to finding the ANN-WOA algorithm capacity in predicting the future price of WTI oil. ANN-WOA model improved the WTI price predicting accuracy up to 22% compared to the ANN. The ANN-WOA method with a value of R2 = 0.93 compared to the ANN method with a value of R2 = 0.75 was able to reduce the model error well. According to the significant impact that the input parameters of the combination model had on the WTI oil price prediction, therefore, in studies that predict price or other variables, highly correlated variables can significantly increase the accuracy of the forecast.

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来源期刊

Energy Sources Part B-Economics Planning and Policy ENERGY & FUELS-

CiteScore

6.80

自引率

12.80%

发文量

审稿时长

6-12 weeks

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