Apr 2024

A LSTM-Based Tesla Stock Prediction Model

Undergraduate Graduation Project

– Author： Zhiyuan

Abstract

This paper aims to propose a method for Tesla stock prediction based on Long Short-Term Memory (LSTM) model. We employ a single-step LSTM for immediate prediction and a multi-layer LSTM for extended prediction that can span multiple days into the future. We explore the use of LSTM networks for Tesla stock price prediction. LSTM is a type of neural network specifically designed to identify complex patterns in sequential data. It has shown to be particularly efficient in tasks dealing with time series data, such as predicting stock prices or processing linguistic sequences. Compared to standard feedforward neural networks, LSTMS have feedback connections that allow them to process an entire sequence of data, not just a single data point. For stock price prediction models, this allows the LSTM to predict future prices taking into account the historical order of stock prices. LSTM networks are often used in time series forecasting, especially in stock price forecasting, due to their ability to capture temporal dependencies.

We investigate two common LSTM model architectures, single-layer LSTM and multi-layer LSTM, which adapt to time series data of different complexities. To verify the effectiveness of our proposed Tesla stock prediction model, we conduct data visualization and experiments. By comparing and analyzing the performance of single-layer LSTM and multi-layer LSTM, we reveal their advantages and limitations in Tesla stock price prediction, in contrast, the multi-layer LSTM shows a greater advantage in performance. Our work has important implications for stock market participants and investors to provide predictive information about the future trend of Tesla stock and help them make more informed investment decisions. In addition, this study has academic significance in exploring and applying LSTM networks in the field of time series prediction, which helps to deeply understand and improve the performance of stock price prediction models. Through the research and analysis in this paper, we derive useful insights and methods to provide guidance for research in Tesla stock prediction and other related fields.

KEY WORDS: Prediction Model, LSTM, Stock Price Prediction, Single-Step LSTM, Stacked LSTM

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