Stock forecasting based on ARIMA model—taking bank of China as an example
ARIMA(auto regressive integrated moving average)model is a statistical model widely used in time series analysis and forecasting.Its core idea is to decompose the time series into an autoregressive(AR)part,a difference(I)part and a moving av-erage(MA)part,which can capture the trend and periodicity in the time series.The time series analysis using ARIMA model was conducted by using the stock closing price data of Bank of China during the period of January 3,2023 to November 30,2023 to fore-cast the stock closing price for the next 31 trading days,which provides investors with important information about the future trend of Bank of China's stock.
万方数据
