Hybridization of hybrid structures for time series forecasting: A review
Z Hajirahimi, M Khashei - Artificial Intelligence Review, 2023 - Springer
Achieving the desired accuracy in time series forecasting has become a binding domain,
and developing a forecasting framework with a high degree of accuracy is one of the most …
and developing a forecasting framework with a high degree of accuracy is one of the most …
Energy price prediction using data-driven models: A decade review
The accurate prediction of energy price is critical to the energy market orientation, and it can
provide a reference for policymakers and market participants. In practice, energy prices are …
provide a reference for policymakers and market participants. In practice, energy prices are …
A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting
Wind speed forecasting is gaining importance as the share of wind energy in electricity
systems increases. Numerous forecasting approaches have been used to predict wind …
systems increases. Numerous forecasting approaches have been used to predict wind …
A combined forecasting model for time series: Application to short-term wind speed forecasting
Wind speed forecasting has been growing in popularity, owing to the increased demand for
wind power electricity generation and developments in wind energy competitiveness. Many …
wind power electricity generation and developments in wind energy competitiveness. Many …
The volatility connectedness of the EU carbon market with commodity and financial markets in time-and frequency-domain: the role of the US economic policy …
OB Adekoya, JA Oliyide, A Noman - Resources Policy, 2021 - Elsevier
This study examines the transmission of volatility risks between the EU carbon market and
various commodity and financial markets across different frequency bands, while accounting …
various commodity and financial markets across different frequency bands, while accounting …
Carbon trading volume and price forecasting in China using multiple machine learning models
Motivated by reducing carbon emissions, carbon trading market have been opened to
promote environmental protection. Accurate carbon trading volume and price forecasts have …
promote environmental protection. Accurate carbon trading volume and price forecasts have …
Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms
Accurate prediction of carbon price effectively ensures the stability of the carbon trading
market and reduces carbon emissions. However, making accurate prediction is challenging …
market and reduces carbon emissions. However, making accurate prediction is challenging …
Carbon price forecasting system based on error correction and divide-conquer strategies
X Niu, J Wang, L Zhang - Applied Soft Computing, 2022 - Elsevier
Carbon price forecasting is an important component of a sound carbon price market
mechanism. The accurate prediction of carbon prices is an active topic of research …
mechanism. The accurate prediction of carbon prices is an active topic of research …
Impacts of haze pollution on China's tourism industry: a system of economic loss analysis
Haze pollution not only negatively influences public health but also causes great economic
losses. However, most previous studies have mainly focused on health-related economic …
losses. However, most previous studies have mainly focused on health-related economic …
A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China
H Shi, A Wei, X Xu, Y Zhu, H Hu, S Tang - Journal of Environmental …, 2024 - Elsevier
Accurately predicting carbon trading prices using deep learning models can help
enterprises understand the operational mechanisms and regulations of the carbon market …
enterprises understand the operational mechanisms and regulations of the carbon market …