基于ARIMA模型的大同市空气质量预测研究

Research on Air Quality Prediction in DaTong Based on ARIMA Model

时序模型作为一种预测方法,在货运量预测、机场客流量预测、疾病发病率预测、空气质量预测等许多重要的领域具有广泛的应用。本文利用大同市2016年1月到2019年8月共44个月的空气质量综合指数数据样本,使用牛顿插值进行了缺失值插补,根据给定的数据序列进行了时序图、自相关图和偏自相关图的构建。然后,进行单位根检验,判断出序列为平稳非白噪声序列。本文使用相对最优模型识别方法确立模型的p、q值,最终建立ARIMA(2,0,1)模型,对2019年9-12月的空气质量综合指数进行预测。通过对模型的分析,判断预测值比较准确。

As a forecasting method,time series model has been widely used in many important fields,such as cargo volume prediction,airport passenger flow prediction,disease incidence prediction,and air quality prediction.In this paper,44 months of air quality composite index data samples from January 2016 to August 2019 in datong city were used to carry out missing value interpolation with Newton interpolation,and time sequence,autocorrelation and partial autocorrelation were constructed according to the given data sequence.Then,the unit root test is carried out and the sequence is determined to be a stationary non-white noise sequence.In this paper,The relative optimal model identification method was used to establish the p and q values of the model,and finally the ARIMA(2,0,1)model was established to predict the air quality index from September to December 2019.Through the analysis of the model,the prediction value is more accurate.