Using computational intelligence to forecast carbon prices

Atsalakis, George S.

doi:10.1016/j.asoc.2016.02.029

Cited by 109 publications

(41 citation statements)

References 39 publications

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“…Recently, research has found that an integrated model that combines the EMD (Empirical model decomposition) method with ANN and LSSVM can achieve better performance for forecasting the carbon price than that of the EMD method alone [21]. Additionally, Atsalakis [22] proposed a computational intelligence-based model with a novel hybrid neuro-fuzzy controller for forecasting the carbon price, which obtained a higher accuracy. Zhu et al [23] combined variational mode decomposition (VMD) and spiking neural networks (SNNs) to improve forecasting accuracy and reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network

Yun

Zhang

et al. 2020

Sustainability

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Predicting the carbon price accurately can not only promote the sustainability of the carbon market and the price driving mechanism of carbon emissions, but can also help investors avoid market risks and increase returns. However, previous research has only focused on the low-order moment perspective of the returns for predicting the carbon price, while ignoring the shock of extreme events and market asymmetry originating from its pricing factor markets. In this paper, a novel extended higher-order moment multi-factor framework (EHM-APT) was formed to improve the prediction and to capture the driving mechanism of the carbon price. Furthermore, a multi-layer and multi-variable Long Short-Term Memory Network (Multi-LSTM) model was constructed so that the parameters and structure could be determined experimentally for testing the performance of the proposed framework. The results show that the pricing framework considers the shock of extreme events and market asymmetry and can improve the prediction compared with a framework that does not consider the shock of higher-order moment terms. Additionally, the Multi-LSTM model is more competitive for prediction than other benchmark models. This conclusion proves the rationality and accuracy of the proposed framework. The application of the pricing framework encourages investors and financial institutions to pay more attention to the pricing factor of extreme events and market asymmetry for accurate price prediction and investment analysis.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network

Yun

Zhang

et al. 2020

Sustainability

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“…(3) Estimating the bandwidth by the H1 Gaussian smoothness. The decomposition process is realized by solving the following optimization problem [22]:…”

Section: Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm

et al. 2019

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The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China’s three major regional emission exchanges.

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“…Zhu and Wei [39], presented several hybrid models of autoregressive integrated moving average model (ARIMA) and least square support vector machine (LSSVM) to predict the carbon futures prices of EU ETS from April 2005 to March 2011, and concluded that the model ARIMALSSVM2 exceeded the single ARIMA, ANN, and LSSVM models. Also, a novel hybrid neuro-fuzzy controller model, based on an ANN system and an adaptive neuro-fuzzy inference system (ANFIS), was provided by Atsalakis to predict the carbon futures price, which was superior to other models such as ARIMA, ANN, ARIMALSSVM2, etc., in terms of forecasting accuracy [40]. Fan et al [41] examined the multilayer perceptron (MLP) neural network for forecasting carbon price.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Zhou

Huo

et al. 2019

Energies

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Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

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Using computational intelligence to forecast carbon prices

Cited by 109 publications

References 39 publications

A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network

A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network

Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm

Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

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