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A new hybrid optimization ensemble learning approach for carbon price forecasting
- Source :
- Applied Mathematical Modelling. 97:182-205
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is usually ignored. Considering the matching utility with deeper understanding of data and model, this paper proposes a novel approach to forecast carbon price, which combines the data preprocessing mechanism, decomposition technology, forecast module with selection and matching strategy and ensemble model based on an original hybrid optimization algorithm. According to a comprehensive evaluation index in consideration of several evaluation perspectives, the optimal parameter structures of the three forecast models are selected in this framework. Then, the data components decomposed by variational mode decomposition are reconstructed into three novel range entropy series with different levels of complexity by range entropy. As a result, the matching relation between the three forecasting models and the three range entropy series is correspondingly established. Additionally, a feedback neural network optimized by hybrid optimization algorithm, which persists more superiorities of reasonable weight assignment than the usual ensemble method, is initially used to synthesize three forecasting results of range entropy series. The carbon price data from four different trading markets in China is used to test the novel approach and the experimental results indicate that it does enhance the performance of carbon price forecasting, and provide a convincing tool for the operation and investment of the carbon markets.
- Subjects :
- Mathematical optimization
Matching (statistics)
Artificial neural network
Ensemble forecasting
Computer science
Applied Mathematics
Stability (learning theory)
02 engineering and technology
01 natural sciences
Ensemble learning
020303 mechanical engineering & transports
0203 mechanical engineering
Carbon price
Modeling and Simulation
0103 physical sciences
Entropy (information theory)
Data pre-processing
010301 acoustics
Subjects
Details
- ISSN :
- 0307904X
- Volume :
- 97
- Database :
- OpenAIRE
- Journal :
- Applied Mathematical Modelling
- Accession number :
- edsair.doi...........f0177b34b768632147466481c397d04f