An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting

Jujie Wang; Xin Sun; Qian Cheng; Quan Cui

doi:10.1016/j.scitotenv.2020.143099

An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting

Sci Total Environ. 2021 Mar 25:762:143099. doi: 10.1016/j.scitotenv.2020.143099. Epub 2020 Oct 16.

Authors

Jujie Wang¹, Xin Sun², Qian Cheng², Quan Cui²

Affiliations

¹ School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China. Electronic address: jujiewang@126.com.
² School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.

PMID: 33127140
DOI: 10.1016/j.scitotenv.2020.143099

Abstract

Carbon price is the basis of developing a low carbon economy. The accurate carbon price forecast can not only stimulate the actions of enterprises and families, but also encourage the study and development of low carbon technology. However, as the original carbon price series is non-stationary and nonlinear, traditional methods are less robust to predict it. In this study, an innovative nonlinear ensemble paradigm of improved feature extraction and deep learning algorithm is proposed for carbon price forecasting, which includes complete ensemble empirical mode decomposition (CEEMDAN), sample entropy (SE), long short-term memory (LSTM) and random forest (RF). As the core of the proposed model, LSTM enhanced from the recurrent neural network is utilized to establish appropriate prediction models by extracting memory features of the long and short term. Improved feature extraction, as assistant data preprocessing, represents its unique advantage for improving calculating efficiency and accuracy. Removing irrelevant features from original time series through CEEMDAN lets learning easier and it's even better for using SE to recombine similar-complexity modes. Furthermore, compared with simple linear ensemble learning, RF increases the generalization ability for robustness to achieve the final nonlinear output results. Two markets' real data of carbon trading in china are as the experiment cases to test the effectiveness of the above model. The final simulation results indicate that the proposed model performs better than the other four benchmark methods reflected by the smaller statistical errors. Overall, the developed approach provides an effective method for predicting carbon price.

Keywords: Carbon price prediction; Hybrid model; Improved feature extraction; Long short-term memory network; Nonlinear ensemble algorithm; Random forest.