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

Zhang, Tingting; Tang, Zhenpeng

doi:10.3389/fenrg.2022.991570

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…LSTM networks introduce memory modules in the various neural nodes of their hidden layers, thereby addressing the gradient problem during iterations of an RNN [36]. Figure 1 shows the design of the LSTM network; the expression for the gate control system operation process in the LSTM network is as follows:…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

A Multi-Strategy Integration Prediction Model for Carbon Price

Dong

Yang

et al. 2023

Energies

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Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China’s major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions.

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Section: Long Short-term Memory Networkmentioning

confidence: 99%

A Multi-Strategy Integration Prediction Model for Carbon Price

Dong

Yang

et al. 2023

Energies

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An optimized LSTM model for clean coal ash content prediction in dense medium separation scenarios on the basis of the dual decomposition method

Liu,

Fan,

et al. 2024

International Journal of Coal Preparation and Utilization

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Investigating the Dynamic Relationship Between Greenhouse Gas Emissions and Gross Domestic Product in Türkiye

ŞAHİN,

GAYAKER

2023

Ankara Hacı Bayram Veli Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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This study aims to investigate the causal relationship between Gross Domestic Product and greenhouse gas emissions in Türkiye from 1951 to 2018, using the Causal Decomposition Method that integrates Ensemble Empirical Mode Decomposition, Hilbert-Huang Transform, and Phase Coherence Methods. The primary focus is on identifying the key sectors contributing significantly to greenhouse gas emissions, particularly those connected to industrial production. The analysis reveals a one-way, short-term causal relationship from Gross Domestic Product to greenhouse gas emissions, spanning approximately 3 years. This finding suggests that changes in Gross Domestic Product have short-term effects on emissions, but not vice versa. Special emphasis is placed on the gases Cardon Dioxide, Methane and Nitrous Oxide, as they demonstrate a strong, consistent causal connection with Gross Domestic Product. The significance of this study lies in its utilization of the Ensemble Empirical Mode Decomposition approach to investigate this dynamic causality and address a notable gap in the existing literature. Empirical results indicate a complex yet observable association between Gross Domestic Product growth and greenhouse gas emissions in Türkiye, and that this relationship becomes more important, especially in the short and long term, with periodic fluctuations.

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

Cited by 3 publications

References 60 publications

A Multi-Strategy Integration Prediction Model for Carbon Price

A Multi-Strategy Integration Prediction Model for Carbon Price

An optimized LSTM model for clean coal ash content prediction in dense medium separation scenarios on the basis of the dual decomposition method

Investigating the Dynamic Relationship Between Greenhouse Gas Emissions and Gross Domestic Product in Türkiye

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