Financial Price Prediction Based on CEEMD and Multi-Channel LSTMs

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外文摘要：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。

外文关键词：

CEEMDPSOLSTMCombination modelAsset investment

作者：

Yang Liu、Ziqian Zeng、Ruikun Li

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作者单位：

Chongqing University of Posts and Telecommunications, 2 Chongwen Road, South Bank District, Chongqing, China 400065

Liaoning University, No. 58 Daoyi South Street, Shenbei New District, Shenyang, Liaoning, China 110136

会议名称：

International Conference on Artificial Intelligence and Intelligent Information Processing

会议地点：

Qingdao(CN)

会议母体文献：

International Conference on Artificial Intelligence and Intelligent Information Processing

页码：

124561G.1-124561G.10

出版时间：

2022

DOI：

10.1117/12.2659983