基于ARIMA模型的股票预测--以中国银行为例  

Stock forecasting based on ARIMA model—taking bank of China as an example

作　　者：周静雯 Zhou Jingwen(School of Computer Science,University of South China,Hengyang 421001,China)

机构地区：[1]南华大学计算机学院,衡阳421001

出　　处：《现代计算机》2024年第14期89-92,共4页Modern Computer

摘　　要：ARIMA模型是一种广泛用于时间序列分析和预测的统计模型。它的核心思想是将时间序列分解成自回归(AR)部分、差分(I)部分和移动平均(MA)部分,从而能够捕捉时间序列中的趋势和周期性。通过中国银行在2023年1月3日至2023年11月30日期间的股票收盘价数据,采用ARIMA模型进行了时间序列分析,对未来31个交易日股票收盘价格进行预测,为投资者提供了有关中国银行股票未来走势的重要信息。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.

关 键 词：中国银行 ARIMA模型预测 股票 

分 类 号：F832.51[经济管理—金融学] F832.33F224