基于LSTM和灰色模型的股价时间序列预测研究

影响股价的因素错综复杂,因此在考虑多变量情形下,对时间序列中常用的长短期记忆网络（LSTM）进行修正,并选取股票价格进行预测.首先,采用方差膨胀因子（VIF）进行变量的筛选,再结合自适应提升法（Adaboost）模型查看特征变量的重要程度.其次,用爬虫对投资者情绪进行文本分析,计算情绪指数等指标并揭示其与股价的关系.然后,对格力电器、飞科电器、美的集团3支股票进行股价预测,对比多层感知器（MLP）模型、LSTM模型,并选择适当的模型作为基准模型,在基准模型的基础上加上情绪指数、投资者关注度等指标构建了LSTM-EM模型.进一步,在考虑了投资者情绪后对残差项使用GM （1,1）模型进行修正.实证结果表明,该模型能对股价进行较为精确的预测.

Stock price time series prediction based on LSTM and grey model

In view of the complicated factors influencing the stock price, we revised the Long Short-Term Memory (LSTM) network, which is commonly used in time series, to predict stock prices under the condition of multivariable.First, the Variance Inflation Factor (VIF) was used to screen variables, and then the adaptive promotion (Adaboost) model was combined to check the importance of characteristic variables.Second, the crawler was used to conduct text analysis of investor sentiment, calculate indicators including sentiment index, and reveal the relationship between them and stock price.Then, prices of three stocks including Gree Electric Appliances, Flyco Electric Appliances and Midea Group were predicted by Multilayer Perceptron (MLP) and LSTM, and the appropriate model was selected as the benchmark model.Finally, indicators of sentiment index and investor concern were added to the benchmark model to construct the LSTM-EM model, and the GM (1, 1) model was used to correct the residual term after considering investor sentiment.The empirical results show that the proposed model can predict the stock price accurately.