基于ARIMA模型的深圳市空气质量指数时间序列预测研究

牟敬锋; 赵星; 樊静洁; 严宙宁; 严燕; 曾丹; 罗文亮; 范志伟

doi:10.13421/j.cnki.hjwsxzz.2017.02.004

基于ARIMA模型的深圳市空气质量指数时间序列预测研究

Time Series Prediction of AQI in Shenzhen Based on ARIMA Model

摘要

摘要:
目的构建适合深圳市空气质量指数（AQI）预测的自回归移动平均模型（ARIMA），为有效地治理和控制空气污染提供科学依据。
方法应用时间序列分析方法对深圳市2014年1月1日-2016年6月30日AQI逐日数据进行分析并建立预测模型，对建立的预测模型进行参数估计、模型诊断、模型评价，选择最优预测模型，利用所得到的模型对2016年7月1日-2016年7月6日AQI进行预测，并评价其预测效果。
结果本研究2014年1月-2016年6月共收集了深圳市912个逐日AQI数据，空气质量级别为优、良和轻度污染的比例分别是48.6%、48.4%和3.0%。经平稳性检验，该原始序列适合进行模型拟合，经过模型拟合诊断发现ARIMA（3，0，1）模型为最优模型，赤池信息准则（AIC值）和贝叶斯信息准则（BIC值）最小，分别为7 364.51和7 393.41，Box-Ljung检验结果Q值为17.48，P>0.05，模型残差为白噪声序列。2016年7月1日-2016年7月6日AQI预测值与实际值的平均相对误差为16.6%，实际值都在95%可信区间内，建立的ARIMA（3，0，1）模型的拟合精度和预测效果较为理想。
结论 ARIMA（3，0，1）模型能较好地模拟深圳市AQI变化趋势，有良好的预测效果。

Abstract:
Objective To establish appropriate prediction model of Air Quality Index(AQI) in Shenzhen on autoregressive integrated moving average (ARIMA) model, and provide a scientific basis for control of air pollution.
Methods Time series analysis was conducted by using the daily data of AQI in Shenzhen from January 1, 2014 to June 30, 2016, and a predictive model was established after parameter estimation, model diagnosis and model evaluation.The optimal prediction model was selected. The model was used to predict the value of AQI from July 1, 2016 to July 6, 2016, and the prediction effect was evaluated.
Results 912 daily AQI values were collected from January 2014 to June 2016 in Shenzhen. The proportion of air quality levels for optimal, good and mild pollution were 48.6%, 48.4% and 48.6% respectively. Through the test of stationarity, ARIMA(3, 0, 1) was selected as the optimal model. The AIC and BIC of this model were 7 364.51 and 7 393.41 respectively, which were the least. The Q statistic was 17.48 (P>0.05) by Box-Ljung testing, indicating the applicability of the model. The average relative error between the predictive value and the actual value of AQI from July 1, 2016 to July 6, 2016 was 16.6%.The actual values were within 95% CI of the predictive values. The established ARIMA(3, 0, 1) model was good in fitting precision and prediction effect.
Conclusion The ARIMA(3, 0, 1) model could predict the change trend of AQI in Shenzhen with good prediction effect.

HTML全文

参考文献(27)

施引文献

资源附件(0)