Abstract
With the development of the carbon market in China, research on the carbon price has received more and more attention in related fields. However, due to its nonlinearity and instability, the carbon price is undoubtedly difficult to predict using a single model. This paper proposes a new hybrid model for carbon price forecasting that combines fast ensemble empirical mode decomposition, sample entropy, phase space reconstruction, a partial autocorrelation function, and an extreme learning machine that has been improved by particle swarm optimization. The original carbon price series is decomposed using the fast ensemble empirical mode decomposition and sample entropy methods, which eliminate noise interference. Then, the phase space reconstruction and partial autocorrelation function methods are combined to determine the input and output variables in the forecasting models. An extreme learning machine optimized by particle swarm optimization was employed to forecast carbon prices. An empirical study based on carbon prices in three typical regional carbon markets in China found that this new hybrid model performed better than other comparable models.
Highlights
Nowadays, climate change has seriously threatened sustainable human development
The results showed that the selected model performed significantly better than single autoregressive integrated moving average (ARIMA) and least squares support vector machine (LSSVM) models by using data collected from the E.U
The promotion of the carbon market is a requirement for the high-quality development of
Summary
Climate change has seriously threatened sustainable human development. Especially, China, as the world’s biggest emitter of CO2 , is concerned in this regard [1]. The results indicated that the proposed EMD-PSO-SVM model performed better than other artificial neural networks (ANNs) in carbon price forecasting. Basing on the data from the E.U. ETS, the empirical results showed that the EMD-LSSVR model performed the best in comparison with other prediction models according to the values of statistical indicators. Thanks to carbon prices having dynamic and nonlinear properties that are similar to those of wind speed, this paper proposes EMD and FEEMD to decompose a carbon price series and introduces both a phase space reconstruction theory (PSR) and a partial autocorrelation function (PACF) for the analysis of the decomposed subsequences. The main contribution of this paper is this new hybrid combination model for carbon price prediction, which is expressed as FEEMD-PSR-PACF-PSO-ELM.
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