用混合LSTM-GNN模型结合时间与股票关系数据，提升股市预测精度。
Stock Price Prediction Using a Hybrid LSTM-GNN Model: Integrating Time-Series and Graph-Based Analysis
- 融合LSTM时序建模与GNN关系分析，捕捉股价动态与关联依赖
- 在真实历史数据上实现0.00144的均方误差，比单独LSTM降低10.6%
- 适合量化交易、金融分析等需实时预测的场景
本文提出一种新颖的混合模型，将长短期记忆网络（LSTM）与图神经网络（GNN）结合，显著提升股市预测准确性。LSTM组件有效捕捉股价数据的时间序列动态，而GNN组件通过皮尔逊相关性和关联分析建模股票间的相互关系，捕捉影响股价的复杂非线性多体依赖。采用扩展窗口验证方法进行训练与评估，支持随数据量增长持续学习并适应市场变化。在历史股票数据上的大量实验表明，该混合模型均方误差（MSE）为0.00144，较独立LSTM模型的0.00161降低10.6%。此外，其性能优于线性回归、卷积神经网络（CNN）及全连接网络等传统与先进基准模型。结果充分证明，结合时间序列与关系数据的混合方法具有显著潜力，为实时交易与金融分析提供强大工具。
原文摘要 · Abstract (English)
This paper presents a novel hybrid model that integrates long-short-term memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the accuracy of stock market predictions. The LSTM component adeptly captures temporal patterns in stock price data, effectively modeling the time series dynamics of financial markets. Concurrently, the GNN component leverages Pearson correlation and association analysis to model inter-stock relational data, capturing complex nonlinear polyadic dependencies influencing stock prices. The model is trained and evaluated using an expanding window validation approach, enabling continuous learning from increasing amounts of data and adaptation to evolving market conditions. Extensive experiments conducted on historical stock data demonstrate that our hybrid LSTM-GNN model achieves a mean square error (MSE) of 0.00144, representing a substantial reduction of 10.6% compared to the MSE of the standalone LSTM model of 0.00161. Furthermore, the hybrid model outperforms traditional and advanced benchmarks, including linear regression, convolutional neural networks (CNN), and dense networks. These compelling results underscore the significant potential of combining temporal and relational data through a hybrid approach, offering a powerful tool for real-time trading and financial analysis.
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