本论文旨在探索基于长短期记忆（LSTM）模型的特斯拉股票预

测方法，并ᨀ 出了一种有效的预测模型。股票预测对于投资者和市

场参与者具有重要意义，因此寻找准确可靠的预测方法至关重要。

LSTM是一种适用于处理时间序列数据的神经网络模型，其具有识别

复杂模式的能力和处理时间依赖性的优势。然而，现有的研究仍存

在一些问题，需要进一步探索和改进。因此，本文ᨀ 出了一种基于

单步LSTM的即时预测和多层LSTM的扩展预测方法，以跨越多天进行

预测。通过对比单层LSTM和多层LSTM的性能，我们揭示了它们在特

斯拉股价预测中的优势和局限性。实验证明，多层LSTM模型在性能

上具有更大的优势。本研究的贡献在于为投资者ᨀ 供了关于特斯拉

股票未来走势的预测信息，帮助他们做出明智的投资决策。此外，

本文对于探索和应用LSTM网络在时间序列预测领域具有学术意义，

并为改进股票价格预测模型的性能ᨀ 供了有益的见解和方法。

This paper aims to explore the prediction method of Tesla stock based on Long Short-Term Memory (LSTM) model, and proposes an effective prediction model. Stock forecasting is of great significance to investors and market participants, so it is crucial to find accurate and reliable forecasting methods. LSTM is a neural network model suitable for processing time series data, which has the ability to recognize complex patterns and the advantage of handling time dependence. However, there are still some problems in the existing research, which need to be further explored and improved. Therefore, this paper proposes an immediate prediction method based on single-step LSTM and an extended prediction method based on multi-layer LSTM to make predictions across multiple days. By comparing the performance of single-layer and multi-layer LSTMS, we reveal their advantages and limitations in Tesla stock price prediction. Experiments show that the multi-layer LSTM model has greater advantages in performance. The contribution of this study is to provide investors with predictive information about the future trend of Tesla stock to help them make informed investment decisions. In addition, this paper has academic significance for exploring and applying LSTM networks in the field of time series prediction, and provides useful insights and methods for improving the performance of stock price prediction models.