用注意力模型提升股市预测，比传统RNN更准
Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies
- 采用注意力机制捕捉股市数据的长短时依赖关系
- 注意力模型在测试中准确率最高，优于LSTM和GRU
- 适合想用AI优化实时交易策略的研究者和开发者
本文系统研究了基于机器学习与深度学习的股价预测方法，评估了多种循环神经网络架构，包括长短期记忆网络（LSTM）、门控循环单元（GRU）及注意力机制模型。这些模型被用于捕捉股市数据中复杂的时序依赖性。实验结果表明，注意力机制模型表现最佳，能够有效同时捕捉短期与长期依赖，实现最高的预测准确率。研究为人工智能驱动的金融预测提供了重要洞见，对构建更精准高效的实时交易系统具有实践指导意义。
原文摘要 · Abstract (English)
This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the performance of various recurrent neural network (RNN) architectures, including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and attention-based models. These models are assessed for their ability to capture complex temporal dependencies inherent in stock market data. Our findings show that attention-based models outperform other architectures, achieving the highest accuracy by capturing both short and long-term dependencies. This study contributes valuable insights into AI-driven financial forecasting, offering practical guidance for developing more accurate and efficient trading systems.
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