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

Xiong, Shenghua; Wang, Chunfeng; Zhen-ming, Fang; Ma, Dan

doi:10.3390/en12010147

Cited by 21 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The forecasting process includes three parts, namely, feature extraction, forecasting, and integrated forecasting, and the RMSE of this model is 1.048. Xiong et al [40] predicted the carbon price in Guangdong by using the VMD-FMRVR-MOWOA mixed model and proved that the model has a good prediction effect, with an RMSE of 0.57. The verification shows that EEMD-LDWPSO-wLSSVM is an effective method to predict carbon price.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Carbon Price in China: A Multimodel Comparison

Huang

Zhou

et al. 2022

IJERPH

View full text Add to dashboard Cite

With the global concern for carbon dioxide, the carbon emission trading market is becoming more and more important. An accurate forecast of carbon price plays a significant role in understanding the dynamics of the carbon trading market and achieving national emission reduction targets. Carbon prices are influenced by many factors, which makes carbon price forecasting a complicated problem. In recent years, deep learning models are widely used in price forecasting, because they have high forecasting accuracy when dealing with nonlinear time series data. In this paper, Multivariate Long Short-Term Memory (LSTM) in deep learning is used to forecast carbon prices in China, which takes into account the factors affecting the carbon price. The historical time series data of carbon prices in Hubei (HBEA) and Guangdong (GDEA) and three traditional energy prices affecting carbon prices from 5 May 2014 to 22 July 2021 are collected to form two data sets. To prove the forecast effect of our model, this paper not only uses Multivariate LSTM, Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Recurrent Neural Network (RNN) to forecast the same data, but also compares the forecast results of Multivariate LSTM with the existing research on HBEA and GDEA forecast based on deep learning recently. The results show that the MAE, MSE, and RMSE obtained by the Multivariate LSTM are all smaller than other prediction models, which proves that the model is more suitable for carbon price forecast and offers a new approach to carbon prices forecast. This research conclusion also provides some policy implications.

show abstract

Section: Discussionmentioning

confidence: 99%

Forecasting Carbon Price in China: A Multimodel Comparison

Huang

Zhou

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Xiong et al 63 proposed the VMD‐FMRVR‐MOWOA model for multistep ahead carbon price prediction. They used VMD to decompose the original price series, followed by using fast multi‐output relevance vector regression (FMRVR) to predict the moving window subsequences.…”

Section: Discussionmentioning

confidence: 99%

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

Duan,

Zhang,

Wang

et al. 2024

Energy Science & Engineering

View full text Add to dashboard Cite

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices.

show abstract

“…In different cases, the choice of method for the three steps varies, but they all aim to bring a competitive accuracy to carbon market prediction. Some advanced signal decomposition methods, such as EMD [13], VMD [14], and CEEMDAN [3], have been introduced to decompose the original time series into several independent series of simple patterns, and then different prediction methods are used to predict the decomposed sequences separately. Some researchers have contributed to finding a more suitable prediction model for the second step; for example, Qin et al [15] innovatively adopted Local Polynomial Prediction (LPP) and Sun and Duan [16] used the improved Extreme Learning Machine (ELM).…”

Section: Progress In Carbon Market Predictionmentioning

confidence: 99%

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

Siddik

2023

Energies

View full text Add to dashboard Cite

Recently, carbon price forecasting has become critical for financial markets and environmental protection. Due to their dynamic, nonlinear, and high noise characteristics, predicting carbon prices is difficult. Machine learning forecasting often uses stacked ensemble algorithms. As a result, common stacking has many limitations when applied to time series data, as its cross-validation process disrupts the temporal sequentiality of the data. Using a double sliding window scheme, we proposed an improved stacking ensemble algorithm that avoided overfitting risks and maintained temporal sequentiality. We replaced cross-validation with walk-forward validation. Our empirical experiment involved the design of two dynamic forecasting frameworks utilizing the improved algorithm. This incorporated forecasting models from different domains as base learners. We used three popular machine learning models as the meta-model to integrate the predictions of each base learner, further narrowing the gap between the final predictions and the observations. The empirical part of this study used the return of carbon prices from the Shenzhen carbon market in China as the prediction target. This verified the enhanced accuracy of the modified stacking algorithm through the use of five statistical metrics and the model confidence set (MCS). Furthermore, we constructed a portfolio to examine the practical usefulness of the improved stacking algorithm. Empirical results showed that the improved stacking algorithm could significantly and robustly improve model prediction accuracy. Support vector machines (SVR) aggregated results better than the other two meta-models (Random forest and XGBoost) in the aggregation step. In different volatility states, the modified stacking algorithm performed differently. We also found that aggressive investment strategies can help investors achieve higher investment returns with carbon option assets.

show abstract

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

Cited by 21 publications

References 48 publications

Forecasting Carbon Price in China: A Multimodel Comparison

Forecasting Carbon Price in China: A Multimodel Comparison

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

Contact Info

Product

Resources

About