Abstract
This chapter advances a hybrid forecasting model for the carbon market. The technology is based on Least Squares Support Vector Machines augmented by particle swarm optimization (PSO). This innovation reaches superior forecasting results in a horse-race containing several combinations of ARIMA time series models.
Special thanks to Lili Yuan and Ying-Ming Wei for supporting writing of Chap. 6.
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Zhu, B., Chevallier, J. (2017). Carbon Price Forecasting with a Hybrid ARIMA and Least Squares Support Vector Machines Methodology. In: Pricing and Forecasting Carbon Markets. Springer, Cham. https://doi.org/10.1007/978-3-319-57618-3_6
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DOI: https://doi.org/10.1007/978-3-319-57618-3_6
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