Predicting Tesla’s stock based on machine learning
DOI:
https://doi.org/10.54097/v4g7nt55Keywords:
Predicting Stock, LSTM Model, Machine Learning.Abstract
This article emphasizes that when using economic theories to predict stocks, a certain logical sequence needs to be followed. Among the many well-known stocks, Tesla's stock is highly volatile. If the stock prediction can be relatively accurate, it will better demonstrate the feasibility of using machine learning to predict stocks. Therefore, the paper takes the prediction of Tesla's stock using machine learning as an example. First, machine learning was used to analyze the data distribution, and then it was used to analyze the data relationships. Based on the results of the first two steps, the model needed for prediction was derived, and then the prediction model (LSTM) was used to accurately predict the stock price fluctuations of Tesla over the past five years. The experimental results indicate that the model has good predictive performance. Finally, it points out the current shortcomings of using machine learning to predict stocks and the future directions for improvement.
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