| Citation: | SONG Gang, ZHANG Yunfeng, BAO Fangxun, et al. Stock prediction model based on particle swarm optimization LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2533-2542. doi: 10.13700/j.bh.1001-5965.2019.0388(in Chinese)
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This paper proposes a stock price prediction model based on particle swarm optimization long short-term memory (PSO-LSTM). This model improves and optimizes the LSTM model, which makes it more appropriate for analyzing relationships such as long-term dependency and for solving complex nonlinear problems. Through finding the key parameters in LSTM model by the PSO algorithm with adaptive learning strategy, the stock data feature matches the network topology structure, and the model's prediction accuracy of stock price is improved. In the experiment, PSO-LSTM models are constructed respectively based on the stock datasets from Shanghai, Shenzhen and Hong Kong, and then they are compared to other prediction models. The comparison results show that the PSO-LSTM stock price prediction model achieves higher prediction accuracy and has general applicability.
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