基于粒子群优化LSTM的股票预测模型

为了提高股票时间序列预测精度，增强预测模型结构参数可解释性，提出一种基于自适应粒子群优化（PSO）的长短期记忆（LSTM）股票价格预测模型（PSO-LSTM），该模型在LSTM模型的基础上进行改进和优化，因此擅长处理具有长期依赖关系的、复杂的非线性问题。通过自适应学习策略的PSO算法对LSTM模型的关键参数进行寻优，使股票数据特征与网络拓扑结构相匹配，提高股票价格预测精度。实验分别以沪市、深市、港股股票数据构建了PSO-LSTM模型，并对该模型的预测结果与其他预测模型进行比较分析。结果表明，基于自适应PSO的LSTM股票价格预测模型不但提高了预测准确度，而且具有普遍适用性。 

Stock prediction model based on particle swarm optimization LSTM

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.