Abstract
The forecasting of carbon emissions trading market price is the basis for improving risk management in the carbon trading market and strengthening the enthusiasm of market participants. This paper will apply machine learning methods to forecast the price of China’s carbon trading market. Firstly, the daily average transaction prices of the carbon trading market in Hubei and Shenzhen are collected, and these data are preprocessed by PCAF approach. Secondly, a prediction model based on Radical Basis Function (RBF) neural network is established and it parameters are optimized by Particle Swarm Optimization (PSO). Finally, the PSO-RBF model is validated by actual data and proved that the PSO-RBF model has better prediction effect than BP or RBF neural network in China’s carbon prices prediction, indicating that it has more significant rationality and applicability and deserves further popularization.
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Huang, Y., Liu, H. (2018). Research on Price Forecasting Method of China’s Carbon Trading Market Based on PSO-RBF Algorithm. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_1
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DOI: https://doi.org/10.1007/978-981-13-2826-8_1
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