Prediction of carbon trading price based on ARIMA-BP-SSALSTM dynamic weighted combination model

Wan-bing Cuan; Xue-bin Lü; Chi-yu Liu

doi:10.1117/12.2672610

28 March 2023 Prediction of carbon trading price based on ARIMA-BP-SSALSTM dynamic weighted combination model

Wan-bing Cuan, Xue-bin Lü, Chi-yu Liu

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 1259745 (2023) https://doi.org/10.1117/12.2672610
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

A single prediction model has its own advantages and disadvantages in different aspects. In order to improve the accuracy of carbon trading price prediction, this paper proposes ARIMA-BP-SSALSTM dynamic weighted combination model that combines ARIMA model, BP model and LSTM model optimized by Sparrow Search Algorithm (SSALSTM). Based on information entropy theory, the greater the information entropy, the more the information provided and the greater the weight. This model uses information entropy to assign dynamic weight and weights to obtain the combined prediction value. The empirical results show that the ARIMA-BP-SSALSTM dynamic weighted combination model is more accurate than the ARIMA model, BP model and SSALSTM model.

Citation Download Citation

Wan-bing Cuan, Xue-bin Lü, and Chi-yu Liu "Prediction of carbon trading price based on ARIMA-BP-SSALSTM dynamic weighted combination model", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 1259745 (28 March 2023); https://doi.org/10.1117/12.2672610

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