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Effects simulation of international natural gas prices on crude oil prices based on WBNNK model

机译:基于WBNNK模型的原油价格对国际天然气价格的影响

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摘要

International crude oil prices are very complex nonlinear time series, which are not only affected by the domination of objective economic laws, but also by politics and pricing system. Therefore it is difficult to establish an effective prediction model based on the general time series analysis. So we need to understand the effects of international natural gas prices on crude oil prices to get more accuracy prediction of crude oil prices. In this paper, we build up WBNNK (wavelet-based Boltzmann cooperative neural network and kernel density estimation) model. The international natural gas ad crude oil prices time series is decomposed into approximate components and random components. International natural gas prices could affect the entire world pricing system. The approximate components, which represented the trend of oil price, are predicted with Boltzmann neural network, which is cooperative with international natural gas prices; the random components are predicted with Gaussian kernel density estimation model. In this paper, we analyzed the time-frequency structure of dubieties wavelet transform coefficient modulus for crude oil price time series, and predicted the oil price with Boltzmann neural network and Gaussian kernel density estimation model. The results show that the model has higher prediction accuracy.
机译:国际原油价格是非常复杂的非线性时间序列,不仅受到客观经济法的统治的影响,而且是由政治和定价制度的影响。因此,难以基于一般时间序列分析建立有效的预测模型。因此,我们需要了解国际天然气价格对原油价格的影响,以获得更准确的原油价格准确性预测。在本文中,我们建立了WBNNK(基于小波的Boltzmann合作神经网络和内核密度估计)模型。国际天然气AD原油价格时间序列分解成近似组件和随机组成部分。国际天然气价格可能会影响整个世界定价系统。与国际天然气价格合作,预计近似组成部分代表油价趋势的趋势,这是合作的;用高斯核密度估计模型预测随机分量。在本文中,我们分析了原油价格时间序列的电动大波变换系数模量的时频结构,并预测了具有Boltzmann神经网络和高斯核密度估计模型的油价。结果表明,该模型具有更高的预测精度。

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