基于SARIMA模型的二氧化氮时间序列预测研究
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摘要
基于烟台主城区2010年1月至2018年6月的NO_2浓度数据,利用Eviews统计软件建立了季节自回归移动平均模型(SARIMA),经过序列平稳化、模型识别及模型诊断,SARIMA(2, 0, 3)(1, 1, 1)12模型的相对误差可控制率在5%以内,拟合效果较为理想。拟合及预测结果表明,烟台市主城区NO_2浓度具有季节性特征,呈递增趋势,随着预测步长的延长,预测误差逐渐增大。SARIMA模型目前适合进行短期预测,今后可结合非线性动力学方法.对其进行改进。
Based on NO_2 concentrations in main urban area of Yantai from January 2010 to June 2018, a seasonal autoregressive integrated moving average model(SARIMA) was established with the statistical software Eviews. After smooth sequence, model recognition and diagnosis, the relative error of SARIMA(2, 0, 3)(1, 1, 1)12 model could be controlled within 5% and the fitting results were satisfied. The fitting and predicted results showed that NO_2 concentrations in main urban area of Yantai performed seasonal characteristics and an increasing trend. With the extension of prediction time, the error of SARIMA model increases.Thus, this model was suitable for short-time forecasting. In further study, this model may be improved by combining the theory of non-linear dynamics.
Based on NO_2 concentrations in main urban area of Yantai from January 2010 to June 2018, a seasonal autoregressive integrated moving average model(SARIMA) was established with the statistical software Eviews. After smooth sequence, model recognition and diagnosis, the relative error of SARIMA(2, 0, 3)(1, 1, 1)12 model could be controlled within 5% and the fitting results were satisfied. The fitting and predicted results showed that NO_2 concentrations in main urban area of Yantai performed seasonal characteristics and an increasing trend. With the extension of prediction time, the error of SARIMA model increases.Thus, this model was suitable for short-time forecasting. In further study, this model may be improved by combining the theory of non-linear dynamics.
引文
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[3]王勖之,曾沛,刘永辉.上海市PM2.5浓度的分析与预测[J].数学的实践与认识,2017,47(15):210-217.
[4]王俊.空气污染对成人哮喘患者肺功能与生命质量的影响[D].北京协和医学院;中国医学科学院;清华大学医学部;北京协和医学院中国医学科学院,2017.
[5]杨帆.降雨对大气颗粒物和气态污染物的清除效率及机制[D].南昌大学,2015.
[6]张良.石家庄市"十一五"期间环境空气质量变化趋势分析及预测研究[D].河北科技大学,2013.
[7]杨克磊,张振宇,和美.应用灰色GM(1,1)模型的粮食产量预测研究[J].重庆理工大学学报(自然科学版),2015(4):124-127.
[8]敖希琴.基于SARIMA模型的安徽省CPI预测[J].蚌埠学院学报,2017,6(3):83-86.
[9]马翠红,赵士超.遗传神经网络结合LIBS技术对钢液Mn元素定量分析[J].现代电子技术,2018,41(15):169-173.
[10]Zhang J,Ding W.Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine:The Case of Hong Kong[J].Int J Environ Res Public Health,2017,14(2):114.
[11]熊志斌.基于ARIMA与神经网络集成的GDP时间序列预测研究[J].数理统计与管理,2011,30(2):306-314.
[12]严宙宁,牟敬锋,赵星,等.基于ARIMA模型的深圳市大气PM2.5浓度时间序列预测分析[J].现代预防医学,2018,45(2):220-223,242.
[13]王坤,阮金梅,邓妮.基于SARIMA模型的曲靖市空气质量指数预测[J].曲靖师范学院学报,2018,37(3):25-29.