An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun, Wei; Li, Zhaoqi

doi:10.1002/ese3.799

Cited by 20 publications

(12 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al (2020) took the trading prices of carbon exchanges in different regions of China as the research object, and constructed a mixed model combining the modified ensemble empirical mode decomposition (MEEMD) and the LSTM optimized by the improved whale optimization algorithm (IWOA). Sun and Li (2020) combined CEEMD and LSTM network to forecast price series from China carbon exchanges. Furthermore, Wang et al (2021) introduced a series of models (including fully integrated EMD, sample entropy, LSTM and random forest) to build a new hybrid model in the prediction of price series from different carbon exchanges in China.…”

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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…The carbon exchanging market has turned into a powerful weapon in easing fossil fuel by-products in China, and carbon cost is at the centre of its activity. Thus, the carbon exchanging market fills in as an imperative part in gauging carbon cost precisely ahead of time [ 6 ]. Sun and Li [ 6 ] imaginatively investigated a group of driven long short-term memory network (LSTM) models in light of reciprocal outfit experimental mode decay (ROEMD) for carbon cost gauging, applying it to each of the eight carbon exchanging piloting centres in China.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the carbon exchanging market fills in as an imperative part in gauging carbon cost precisely ahead of time [ 6 ]. Sun and Li [ 6 ] imaginatively investigated a group of driven long short-term memory network (LSTM) models in light of reciprocal outfit experimental mode decay (ROEMD) for carbon cost gauging, applying it to each of the eight carbon exchanging piloting centres in China. ROEMD was first executed for mode change to disintegrate the first convoluted mode into a bunch of straightforward modes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

Tawiah

Daniyal

Qureshi

2023

Journal of Environmental and Public Health

View full text Add to dashboard Cite

Pakistan is considered among the top five countries with the highest CO2 emissions globally. This calls for pragmatic policy implementation by all stakeholders to bring finality to this alarming situation since it contributes greatly to global warming, thereby leading to climate change. This study is an attempt to make a comparative analysis of the linear time series models with nonlinear time series models to study CO2 emission data in Pakistan. These linear and nonlinear time series models were used to model and forecast future values of CO2 emissions for a short period. To assess and select the best model among these linear and nonlinear time series models, we used the root mean square error (RMSE) and the mean absolute error (MAE) as performance indicators. The outputs showed that the nonlinear machine learning models are the best among all other models, having the lowest RMSE and MAE values. Based on the forecasted value of the nonlinear machine learning neural network autoregressive model, Pakistan’s CO2 emissions will be 1.048 metric tons per capita by 2028. The increasing trend in emissions is a frightening and clear warning, suggesting that innovative policies must be initiated to reduce the trend. We encourage the Pakistan government to price CO2 emissions by companies and entities per ton, adapt electricity production from hydro, wind, and different sources with no emissions of CO2, initiate rigorous planting of more trees in the populated areas of Pakistan as forest covers, provide incentives to companies, organisations, institutions, and households to come out with clean technologies or use technologies with no CO2 emissions or those with lower ones, and fund more studies to develop clean and innovative technologies with less or no CO2 emissions.

show abstract

“… The decomposition technology has started to receive attention from researchers in data preprocessing recently. The decomposition-integration model based on the idea of “first decomposition and then integration” has great advantages in dealing with non-stationary and nonlinear data, and its forecasting effect is better [ [21] , [22] , [23] , [24] ]. In order to further improve the prediction accuracy, some scholars have introduced the decomposition-integration model into the case prediction of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases

Yang

Liu

2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Cited by 20 publications

References 54 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

Pakistan CO2 Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases

Contact Info

Product

Resources

About