基于事件LSTM模型的股价走势预测

doi:10.3778/j.issn.1002-8331.2412-0437

摘要/Abstract

摘要： 股票价格走势预测是证券投资领域的关键问题。股价时间序列通常具有非线性、剧烈波动和高噪声等特征，传统预测模型难以有效建模。设计了一种事件LSTM模型，通过改造长短期记忆网络（long short-term memory，LSTM）的细胞结构，将非周期性事件信息和周期性股价信息进行有效融合建模。具体而言，一是在LSTM细胞结构中引入专门的事件控制门，用于处理非周期性事件信息，使模型能够捕捉公司公告等非周期性事件对股价走势的影响。二是在此基础上，利用注意力机制突出历史关键信息的贡献，以缓解噪声数据的干扰，提升股价走势预测的准确性。实验结果表明，提出的模型在预测精度上具有显著优势，并在处理公告事件影响方面表现优异。

关键词: 股价走势预测, 事件建模, 长短期记忆网络, 注意力机制

Abstract: Stock price trend prediction is a critical issue in securities investment. Due to the nonlinear, volatile, and noisy characteristics of stock price sequences, traditional time series forecasting models often struggle to effectively capture these dynamics. To address these challenges, this paper presents an event LSTM model that modifies the cell structure of long short-term memory (LSTM) networks, allowing for the effective integration and modeling of both non-periodic event information and periodic stock price data. Specifically, a dedicated event control gate is incorporated into the LSTM cell structure to process non-periodic event information, enabling the model to effectively capture the impact of such events on stock price trends. Additionally, an attention mechanism is utilized to highlight the contributions of historical key information, thereby reducing the influence of noisy data and achieving more accurate predictions of stock price trends. Experimental results demonstrate that the proposed model significantly outperforms existing models in terms of prediction accuracy, and exhibits exceptional performance in accounting for the effects of announcement events.

Key words: stock price trend prediction, event modeling, long short-term memory network, attention mechanism

吴勰, 赵冠英, 刘宏志, 倪子恒, 孙天琦. 基于事件LSTM模型的股价走势预测[J]. 计算机工程与应用, 2025, 61(22): 295-303.

WU Xie, ZHAO Guanying, LIU Hongzhi, NI Ziheng, SUN Tianqi. Stock Price Trend Prediction Based on Event-Aware LSTM Model[J]. Computer Engineering and Applications, 2025, 61(22): 295-303.

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