基于ARIMA模型的股票价格预测分析
Stock Price Forecasting Analysis Based on ARIMA Model
摘要: 本文立足于对时间序列数据的研究分析，以深粮控股的股价数据为实验对象，采用一种基于差分自回归移动平均(ARIMA)模型对其未来几天的股票价格进行预测。用python和Eviews对采集的股票价格数据进行平稳性检验、白噪声检验、模型定阶、残差检验等步骤。建立了有效预测股票价格的ARIMA模型，同时结合真实值，对模型的有效性进行检验。结果表明，该方法能有效提取原始数据中心的信息，对股票价格预测效果较好。
Abstract:
Based on the research and analysis of time series data, this paper adopts a differential autoregres-sive moving average (ARIMA) model to predict the stock price of SZG Holdings in the coming days based on its stock price data as the experimental object. Firstly, the collected stock price data were tested for smoothness, white noise test, model sizing and residual test by using python and Eviews, and then an ARIMA model was established to effectively predict the stock price, while the validity of the model was tested by combining the true values. The results show that the method can effective-ly extract information from the center of the original data and has a good effect on stock price pre-diction.
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