Research on carbon price volatility prediction based on CEEMDAN and optimized LSTM model
This paper takes the realized volatility of Beijing carbon emission allowance trading prices as the research object,constructs a hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and long short-term memory network(LSTM),and optimizes the model structural parameters through particle swarm optimization(PSO)algorithm.The experimental results demonstrate that the model has the advantages of extracting multi-scale complex time series volatility trends and effectively processing financial time series.The particle swarm optimization algorithm optimizes the structural parameters of the forecasting model to avoid fitting problems caused by improper parameter selection.The model has significant accuracy and stability in forecasting carbon price volatility.
applied statistical mathematicsprediction of carbon price volatilityCEEMDAN-PSO-LSTMtime series forecasting
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