Tesla Stock Price Forecast Based on ARIMA Model and Machine Learning Techniques

One of the most significant aspects of the world economy is the stock market, and the forecasting of stock prices is gaining more and more significant attraction. Under the current international situation, Tesla’s stock price was selected as the research object, and Tesla stock’s open price from July 15, 2020, to July 15, 2024, was chosen as the data set. Auto-Regressive Integrated Moving Average (ARIMA) model and machine learning techniques were taken to forecast the stock price in this paper. Both methods give essentially correct predictions, but there are also some differences between those two methods. The ARIMA model provides statistical trends within a time range, while machine learning techniques provide a stock price prediction that is accurate to the day but has weaker interpretability. By combining the two methods people can get a more accurate forecast of the stock price and then make more informed decisions in the stock market.

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Publication Details

Journal
Science and Technology of Engineering Chemistry and Environmental Protection
Published
2024-12-31
DOI
https://doi.org/10.61173/pf98r920
Primary Topic
Stock Market Forecasting Methods
Type
article
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article

Tesla Stock Price Forecast Based on ARIMA Model and Machine Learning Techniques

Haowen Shi
Science and Technology of Engineering Chemistry and Environmental Protection
Stock Market Forecasting Methods
article

Tesla Stock Price Forecast Based on ARIMA Model and Machine Learning Techniques

Haowen Shi
article en

Abstract

One of the most significant aspects of the world economy is the stock market, and the forecasting of stock prices is gaining more and more significant attraction. Under the current international situation, Tesla’s stock price was selected as the research object, and Tesla stock’s open price from July 15, 2020, to July 15, 2024, was chosen as the data set. Auto-Regressive Integrated Moving Average (ARIMA) model and machine learning techniques were taken to forecast the stock price in this paper. Both methods give essentially correct predictions, but there are also some differences between those two methods. The ARIMA model provides statistical trends within a time range, while machine learning techniques provide a stock price prediction that is accurate to the day but has weaker interpretability. By combining the two methods people can get a more accurate forecast of the stock price and then make more informed decisions in the stock market.

Science and Technology of Engineering Chemistry and Environmental ProtectionVol. 1(10)
Openalex Percentile: Top 69%
Stock Market Forecasting Methods
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