对比LSTM、GRU与Transformer在特斯拉股价预测中的表现，LSTM准确率达94%。
Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction
- 基于2015-2024年特斯拉数据，比较三种序列模型的预测能力
- LSTM模型在股价趋势预测中达到94%的准确率，表现最优
- 适合金融量化研究者参考模型选择与时间序列建模
在快速变化的金融市场中，投资者持续寻求优势以做出明智决策。尽管实现股票价格预测的完美准确度仍具挑战，但人工智能的进步显著提升了我们分析历史数据并识别潜在趋势的能力。本文以人工智能驱动的股票价格趋势预测为核心，构建了2015至2024年特斯拉公司股票的训练数据集，对LSTM、GRU和Transformer模型进行了对比分析。实验结果表明，LSTM模型的预测准确率达到94%，其表现优于其他两种模型。该研究为投资者提供更科学的决策依据，有助于深入理解市场行为。
原文摘要 · Abstract (English)
In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors.
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