ARIMA model, machine learning techniques, Random Forest, stock price forecasting
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.
