Machine Learning in Tesla’s Stock Price Prediction

Abstract

With the development of new energy battery technology and people's attention to environmental issues, new energy vehicles have gradually become one of the important choices for people to travel, and stock investors have also begun to keep an eye on the field of new energy vehicles. However, since the new energy industry is an emerging industry, the current stock market analysis in this regard is not complete. Machine learning is one of the important means of stock forecasting at present. This article will use 6 machine learning models such as linear regression, polynomial regression, XGBoost, ARIMA, Prophet and LSTM to analyze and predict the stock of Tesla, a leading company in the field of new energy vehicles, so as to judge the investment prospects of new energy vehicles. The outcomes demonstrate that LSTM has the lowest error in predicting the price of Tesla’s stock.