Abstract
In this paper, we build up WBNNK model based on wavelet-based cooperative Boltzmann neural network and kernel density estimation. The international oil prices time series is decomposed into approximate components and random components. The approximate components, which represented the trend of oil price, are predicted with Boltzmann neural network; 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 international natural gas and crude oil price time series, and predicted the oil price with cooperative Boltzmann neural network and Gaussian kernel density estimation model.
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