A Hybrid Model for Carbon Price Forecasting Based on Improved Feature Extraction and Non-Linear Integration
Yingjie Zhu,
Yongfa Chen,
Qiuling Hua
et al.
Abstract:Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation… Show more
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