基于循环神经网络的股票预测分析
Stock Prediction Analysis Based on Recurrent Neural Network

随着市场的不确定性和复杂性的增加，传统的投资分析方法已无法完全满足投资者的需求。因此，基于数据分析和机器学习的预测方法逐渐成为投资领域的研究热点。准确的股票价格预测不仅能够帮助投资者制定短期的买入和卖出策略，还能为长期投资决策提供数据支持。本文将基于长短时记忆网络(LSTM)，结合技术指标(如相对强弱指数、布林带以及阿隆振荡器)的可视化研究，对历史股票数据进行深度学习建模，探讨股票价格的变化趋势。
As market uncertainty and complexity increase, traditional investment analysis methods can no longer fully meet the needs of investors. Therefore, prediction methods based on data analysis and machine learning have gradually become a research hotspot in the investment field. Accurate stock price prediction can not only help investors formulate short-term buying and selling strategies, but also provide data support for long-term investment decisions. This paper will conduct deep learning modeling of historical stock data based on the long short-term memory network (LSTM) and the visualization of technical indicators (such as relative strength index, Bollinger bands, and Aroon oscillator) to explore the trend of stock price changes.