Abstract
In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models.
Highlights
The increasing global attention on environmental issues such as climate change in recent decades, has raised interest in understanding the topics related to it
From an academic perspective, our paper contributes to the literature by demonstrating that a new hybrid method that features fuzzy entropy and extreme learning machine is effective in forecasting carbon futures price
We propose a novel hybrid model that builds on ensemble empirical mode decomposition (EEMD) models by incorporating fuzzy entropy (FuzzyEn) and extreme learning machine (ELM) methods
Summary
The increasing global attention on environmental issues such as climate change in recent decades, has raised interest in understanding the topics related to it. Carbon markets are one important mechanism to tackle climate change. A major step was the establishment of the European Union Emissions Trading System (EU ETS) which has become the biggest and most important carbon exchange market in the world; as such it attracts the attention of global organizations to govern carbon emissions as well as international investors as an opportunity for the investment in this market. The determinants together lead to strong fluctuations in the carbon market, which is characterized by chaos and high volatility with nonlinear, non-stationary phenomena evident in its prices. Further understanding the patterns of price fluctuations and predicting movements more accurately is of great importance to practitioners, policymakers and academics. From an academic perspective, our paper contributes to the literature by demonstrating that a new hybrid method that features fuzzy entropy and extreme learning machine is effective in forecasting carbon futures price.
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