用LSTM与梯度提升树融合预测股价，准确率提升10%-15%。
Gradient Boosting Decision Tree with LSTM for Investment Prediction
- 结合LSTM与LightGBM/CatBoost，融合时序与树模型优势
- 相比单模型提升10%-15%准确率，市场波动时误差更小
- 适合金融预测研究者，可灵活接入新算法
本文提出一种混合框架，将LSTM网络与LightGBM、CatBoost结合用于股票价格预测。该框架处理时间序列金融数据，并在七种模型上评估性能：人工神经网络（ANNs）、卷积神经网络（CNNs）、双向LSTM（BiLSTM）、普通LSTM、XGBoost、LightGBM和标准神经网络（NNs）。使用平均绝对误差（MAE）、R平方、均方误差（MSE）和均方根误差（RMSE）等指标，在不同时间尺度上建立基准。基于这些基准，我们构建了一个集成模型，融合序列模型与树模型的优势。实验结果表明，所提框架相比单个模型准确率提升10%至15%，并在市场变化期间降低误差。本研究展示了集成方法在金融预测中的潜力，并提供了可灵活整合新机器学习技术的设计方案。
原文摘要 · Abstract (English)
This paper proposes a hybrid framework combining LSTM (Long Short-Term Memory) networks with LightGBM and CatBoost for stock price prediction. The framework processes time-series financial data and evaluates performance using seven models: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Bidirectional LSTM (BiLSTM), vanilla LSTM, XGBoost, LightGBM, and standard Neural Networks (NNs). Key metrics, including MAE, R-squared, MSE, and RMSE, are used to establish benchmarks across different time scales. Building on these benchmarks, we develop an ensemble model that combines the strengths of sequential and tree-based approaches. Experimental results show that the proposed framework improves accuracy by 10 to 15 percent compared to individual models and reduces error during market changes. This study highlights the potential of ensemble methods for financial forecasting and provides a flexible design for integrating new machine learning techniques.
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