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首页> 外文会议>2010 Sixth International Conference on Natural Computation >Effects simulation of international natural gas prices on crude oil prices based on WBNNK model
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Effects simulation of international natural gas prices on crude oil prices based on WBNNK model

机译：基于WBNNK模型的国际天然气价格对原油价格的影响模拟

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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，不仅受客观经济规律的支配，而且还受到政治和定价体系的影响。因此，难以基于一般的时间序列分析来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，才能更准确地预测原油价格。在本文中，我们建立了WBNNK（基于小波的Boltzmann协作神经网络和核密度估计）模型。国际天然气和原油价格时间序列被分解为近似分量和随机分量。国际天然气价格可能会影响整个世界的定价体系。代表石油价格趋势的近似成分是通过与国际天然气价格合作的玻尔兹曼神经网络进行预测的;用高斯核密度估计模型预测随机分量。本文分析了原油价格时间序列的双重性小波变换系数模量的时频结构，并利用Boltzmann神经网络和高斯核密度估计模型对石油价格进行了预测。结果表明，该模型具有较高的预测精度。

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