A Multivariate Decomposition Ensemble Prediction Method for Carbon Prices Incorporating News Influence Exponential Attenuation
The news covers information closely related to carbon prices,including policies,economics,and energy.Its impact on carbon prices is time-sensitive.To quantify the degree of news influence attenuation,this paper proposes a method for calculating attenuated news influence based on word frequency statistics and exponential decay,which extracts features from news data.The decay influence of news more accurately reflects the extent of its influence on carbon prices.In order to improve prediction accuracy,this paper constructs a multivariate decomposition-ensemble prediction model of carbon prices incorporating news influence exponential attenuation.It applies noise-assisted multivariate empirical mode decomposition method to decompose the data into several subcomponents.Then the subcomponents are reconstructed based on sample entropy.Finally,machine learning methods are applied to predict the subcomponents,which are aggregated to obtain the prediction results.A case study of empirical analysis is conducted using carbon prices of Hubei province.The results show that the news influence exponential attenuation can effectively portray the correlation between news and carbon prices.The proposed multivariate decomposition-ensemble model shows excellent and stable prediction performance.
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