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

Yun, Po; Zhang, Chen; Wu, Yaqi; Yang, Xianzi; Wagan, Zulfiqar Ali

doi:10.3390/su12051869

Cited by 13 publications

(8 citation statements)

References 27 publications

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“…Xu et al (2020) took the carbon price data of the EU ETS as the research object, combined the technology of data reconstruction with extreme learning machine algorithm, and constructed CPN-ELM model to predict the carbon price. Yun et al (2020) introduced an LSTM model with multi-layer and multi-variable characteristics into the prediction research of carbon price.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

Zhang

Tang

2023

Front. Energy Res.

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Numerous studies show that it is reasonable and effective to apply decomposition technology to deal with the complex carbon price series. However, the existing research ignores the residual term containing complex information after applying single decomposition technique. Considering the demand for higher accuracy of the carbon price series prediction and following the existing research path, this paper proposes a new hybrid prediction model VMD-CEEMDAN-LSSVM-LSTM, which combines a new quadratic decomposition technique with the optimized long short term memory (LSTM). In the decomposition part of the hybrid model, the original carbon price series is processed by variational mode decomposition (VMD), and then the residual term obtained by decomposition is further decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In the prediction part of the hybrid model, least squares support vector machine (LSSVM) is introduced, and LSSVM-LSTM model is constructed to predict the components obtained by decomposition. The empirical research of this paper selects two different case data from the European Union emissions trading system (EU ETS) as samples. Taking the results of Case Ⅰ in the 1-step ahead forecasting scenario as an example, the prediction evaluation indexes eMAPE, eRMSE and R2 of the VMD-CEEMDAN-LSSVM-LSTM hybrid model constructed in this paper are 0.3087, 0.0921 and 0.9987 respectively, which are significantly better than other benchmark models. The empirical results confirm the superiority and robustness of the hybrid model proposed in this paper for carbon price forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

Zhang

Tang

2023

Front. Energy Res.

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“…Long Short-Term Memory Network (LSTM) is essentially a specific form of recurrent neural network. LSTM solves the long-term dependence problem of RNN by increasing the gate, and solves the long-term memory deficiency and gradient caused by the multiple multiplication of Jacobian matrix problems such as vanishing or gradient dilation enable recurrent neural networks to truly and effectively utilize long-distance temporal information [33] .…”

Section: The Lstm Prediction Modelsmentioning

confidence: 99%

“…The Adam algorithm combines the advantages of the Ada Grad with RMS Prop algorithms, and uses the first-order moment estimation as well as the second-order moment estimation of the gradient to dynamically adjust the learning rate of each parameter, making the parameter update more stable and occupying less storage resources. An effective gradient-based stochastic optimization method, the formula of Adam's algorithm is as follows [33] :…”

Section: Lstm Model Predictionmentioning

confidence: 99%

Prophet-LSTM combination model carbon trading price prediction research

Dou

Meng

2022

Second International Symposium on Computer Technology and Information Science (ISCTIS 2022)

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Driven by the dual carbon goals, China's carbon trading plays an increasingly significant role in carbon emission reduction. The price of carbon trading is the core issue of carbon trading, and predicting changes in the price of carbon trading is of great significance to the long-term low carbon development of enterprises. Based on an in-depth analysis of the properties of Prophet model and Long Short-Term Memory (LSTM) neural network, a combined Prophet-LSTM model for shortterm prediction of carbon trading price is proposed in this paper. Prophet and LSTM prediction models are built separately, then a combination model is built by optimizing the weight coefficients for carbon trading price prediction. The data of Hubei carbon trading market from 2019 to 2022 were used as an arithmetic example for validation. The experimental results show that the combined Prophet-LSTM forecasting model has stronger stability and higher accuracy than the standard Prophet model forecasting method, LSTM model forecasting method, ARIMA model forecasting method and SARIMAX model forecasting method in carbon trading price time series analysis. The combined forecasting method proposed in this paper can effectively improve the prediction accuracy of carbon trading price, thus helping enterprises to reasonably assess their carbon assets and control the cost of carbon in production to achieve low carbon and high quality development.

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“…The first is the asymmetric distribution of market returns, the tail distribution has the characteristics of left deviation, and the skewness is negative [ 3 , 4 , 5 ]. The second is the high sensitivity to policy events or external events [ 6 ]. For example, the policy implementation of banning interterm storage of carbon quotas led to a serious decline in European carbon price at the end of 2007; the fall in carbon price caused by the expiration of the second phase of emission reduction in Europe at the end of 2012; the outbreak of COVID-19 virus led to global economic downturn and triggered a sharp drop in carbon price.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Yun

Zhang

et al. 2022

IJERPH

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The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

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

Cited by 13 publications

References 27 publications

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

Prophet-LSTM combination model carbon trading price prediction research

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

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