基于K线与均线的CAE和GRU模型的股价预测方法

Stock Price Forecasting Method Based on CAE and GRU Models of K-Line and Moving Average

摘要: 设计快速精确的股票预测方法以实现投资收益最大化是学界的研究热点。然而，现有的股票预测方法忽略了不同K线和均线之间的关联对股价的影响，从而导致预测效果不佳。为了解决该问题，基于CAE和GRU模型提出了一种利用K线和均线特征的关联性预测股价的方法。与选取的现有股票预测方法相比，本模型在上证指数数据集上具有更好的拟合和泛化性能，能够有效地减小预测误差。

Abstract: Designing fast and accurate stock prediction methods to maximize investment returns is a hot re-search topic in academia. However, the existing stock forecasting methods ignore the impact of the correlation between different K-lines and moving averages on stock prices, which leads to poor pre-diction results. To address this problem, based on CAE and GRU model, this paper proposes a method to predict stock price by using the correlation of K-line and moving average characteristics. Compared with the existing stock prediction methods selected, this model has better fitting and generalization performance on the Shanghai Stock index dataset, and can effectively reduce the prediction error.