基于ARIMA-LSTM的股价预测——以贵州茅台为例

Stock Price Prediction Based on ARIMA-LSTM—A Case Study of Kweichow Moutai

摘要: 在经济快速发展和金融市场波动的背景下，股价预测对于投资者、企业管理者以及金融机构来说至关重要。这不仅有助于他们掌握未来的风险，还为投资决策和监管提供了重要依据。单一预测模型在股价预测中很难同时捕获到数据序列中的线性和非线性特征，因此预测效果不理想。针对该问题提出了一种基于ARIMA模型与LSTM模型相结合的股价预测模型，综合考虑线性与非线性特征的股价预测。本文采用贵州茅台(600519) 2018年1月2日到2023年12月29日之间的每个交易日的日收盘价进行实验，实验结果表明，与单一的ARIMA模型和LSTM模型相比，ARIMA-LSTM组合模型在股价预测方面取得了较好的效果。

Abstract: In the context of rapid economic development and fluctuating financial markets, stock price prediction is crucial for investors, corporate managers, and financial institutions. It not only helps them assess future risks but also provides essential support for investment decisions and regulatory actions. A single predictive model often struggles to capture both linear and nonlinear features in stock price data, leading to suboptimal forecasting results. To address this issue, a hybrid stock price prediction model combining the ARIMA model and the LSTM model is proposed, which comprehensively considers both linear and nonlinear characteristics. This study uses the daily closing prices of Kweichow Moutai (600519) from January 2, 2018, to December 29, 2023, for experiments. The experimental results show that the ARIMA-LSTM hybrid model achieves better stock price prediction performance compared to using either the ARIMA or LSTM model alone.