ARIMA model is often used to forecast time series, and stock price prediction has always been a concern of investors. Accurate stock price prediction helps to make reasonable decisions in the unpredictable financial market. By modeling historical data, this paper uses ARIMA model to fit the change law of time series data, and then predict future stock changes. This paper selects the closing price
... [Show full abstract] data of Shanghai Composite Index on all trading days from August 19, 2019, to August 18, 2023, conducts stationarity test on it, completes the identification and sequencing of ARIMA model, conducts model test, analyzes and forecasts stock prices based on this model. The results show that the ARIMA model has a good forecasting effect on the short-term change rule of stock price time series, and has a certain reference significance for investors to make stock investment. The results show that the selected test data fits the ARIMA model well.
