A Novel FD3 Framework for Carbon Emissions Prediction

Monitoring and controlling the carbon emissions need machine learning-based forecasting models at this modern era. Despite various of artificial neural networks (ANNs), we propose a novel FD3 framework to tackle carbon emissions prediction. In our approach, three “FD” procedures are executed: (1) frequency decomposition achieved by using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an advanced version of the famous empirical mode decomposition (EMD); (2) forecasting dendritic neuron model (DNM) that has proved validity on numerous prediction tasks, showing advanced nonlinear fitting ability than traditional network-structured ANNs; and (3) fluctuation density measurement (FD function) that used to regulate the predicting strategy for each decomposed subseries. In experiments, the FD3 framework has shown better performance than seven baseline models in terms of three widely used time series prediction evaluation metrics. The success of our FD3 has confirmed the validity of “preprocessing-forecasting” workflow and provides better solutions for carbon emissions prediction. Furthermore, the design of FD function can give more insights for signal analysis that the selection of decomposed subseries can have huge impacts on the original data.