Financial price prediction based on CEEMD and multi-channel LSTMs

Yang Liu; Ziqian Zeng; Ruikun Li

doi:10.1117/12.2659983

30 November 2022 Financial price prediction based on CEEMD and multi-channel LSTMs

Yang Liu, Ziqian Zeng, Ruikun Li

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124561G (2022) https://doi.org/10.1117/12.2659983
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

Abstract

With the enhancement of people's investment awareness, more and more people invest in bitcoin and gold for profit. Therefore, accurate prediction of Bitcoin and gold price movements is very important for investors. In view of this, this paper innovatively proposes a combined model that uses Complementary Ensemble Empirical Mode Decomposition (CEEMD) to reduce noise and separate long-term trends; and uses a Particle Swarm Optimization combined with Long Short-Term Memory (PSO-LSTM) model to predict price changes. Compared with traditional models, PSO-LSTM has better robustness and generalization ability, and can better extract temporal features. In order to verify the validity of the model, this paper selects the bitcoin and gold price data from September 11, 2016, to September 10, 2021, uses the sliding window method to divide the data set, and finally calculates the MSE, RMSE, MAE, DTC of ARIMA, BP, SVM, LSTM and CEEMD-PSO-LSTM models. Eventually found that the CEEMD-PSO-LSTM had the best accuracy and stability.

Citation Download Citation

Yang Liu, Ziqian Zeng, and Ruikun Li "Financial price prediction based on CEEMD and multi-channel LSTMs", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124561G (30 November 2022); https://doi.org/10.1117/12.2659983

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