基于ARIMA-SVM模型的郑州市CPI预测研究
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摘要
利用ARIMA-SVM组合预测模型对郑州市的月度居民消费者价格指数(CPI)进行预测。首先,分别用ARIMA模型和SVM预测模型对郑州市月度CPI进行预测;然后,采用ARIMA-SVM组合预测模型对其CPI进行预测,并将结果与单独的ARIMA模型和SVM模型进行比较。实证结果表明,ARIMA-SVM模型能更准确地预测郑州市的CPI指数。
The ARIMA-SVM combined forecasting model is applied to forecast the monthly consumer price index of Zhengzhou. Firstly, the monthly CPI of Zhengzhou City is predicted by ARIMA model and SVM model respectively. Then, the ARIMA-SVM combined forecasting model is used to predict CPI, and the data of which are weighed against the individual ARIMA model and SVM model. The results illustrate that the ARIMA-SVM model combined with the two prediction models can predict the CPI of Zhengzhou more accurately.
The ARIMA-SVM combined forecasting model is applied to forecast the monthly consumer price index of Zhengzhou. Firstly, the monthly CPI of Zhengzhou City is predicted by ARIMA model and SVM model respectively. Then, the ARIMA-SVM combined forecasting model is used to predict CPI, and the data of which are weighed against the individual ARIMA model and SVM model. The results illustrate that the ARIMA-SVM model combined with the two prediction models can predict the CPI of Zhengzhou more accurately.
引文
[1] 王宇,李旭东,李自力.基于BP神经网络的我国CPI预测与对策[J].计算机科学,2009,36(10):256-257,264.
[2] 郭晓峰.基于ARIMA模型的中国CPI走势预测分析[J].统计与决策,2012(11):29-32.
[3] 韩春蕾,高婉君.我国月度CPI的组合预测及分析[J].统计与决策,2014(1):11-13.
[4] Chan J C C.Moving Average Stochastic Volatility Models with Application to Inflation Forecast[J].Journal of Econometrics,2013(2):162-172.
[5] 路世昌,赵博琦,毕建武.基于模糊信息粒化SVM时序回归CPI预测[J].统计与决策,2015(14):82-84.
[6] Bel K,Paap R.Modeling the Impact of Forecast-based regime Switches on US Inflation[J].International Journal of Forecasting,2016(4):1306-1316.
[7] 何丹.遗传神经网络模型在CPI预测中的实证检验[J].统计与决策,2017(2):76-78.
[8] 董莉,彭凯越,唐晓彬.大数据背景下的CPI实时预测研究[J].调研世界,2017(8):51-54.
[9] 彭乃驰,党婷.基于小波分析的BP-SARIMA模型的CPI预测[J].统计与决策,2018(16):22-25.
[10] 鲁万波,杨冬.基于半参数混频误差修正模型的中国CPI预测研究[J].统计研究,2018(10):28-43.
[11] 邰晓红,刘义.基于EEMD-PSO-SVM的月度CPI预测研究[J].统计与决策,2019(3):30-33.
[12] Box G E P,Jenkins G M.Time Series Analysis Forecasting and Control-Rev.ed.[J].Journal of Time,1976(4):238-242.
[13] Vapnik V.The Nature of Statistical Learning Theory[M].Springer,1995.
[14] Drucker H,Burges C J C,Kaufman L,Smola A,Vapnik V N.Support Vector Regression Machines[J].Advances in Neural Information Processing Systems,1997(9):155-161.
[2] 郭晓峰.基于ARIMA模型的中国CPI走势预测分析[J].统计与决策,2012(11):29-32.
[3] 韩春蕾,高婉君.我国月度CPI的组合预测及分析[J].统计与决策,2014(1):11-13.
[4] Chan J C C.Moving Average Stochastic Volatility Models with Application to Inflation Forecast[J].Journal of Econometrics,2013(2):162-172.
[5] 路世昌,赵博琦,毕建武.基于模糊信息粒化SVM时序回归CPI预测[J].统计与决策,2015(14):82-84.
[6] Bel K,Paap R.Modeling the Impact of Forecast-based regime Switches on US Inflation[J].International Journal of Forecasting,2016(4):1306-1316.
[7] 何丹.遗传神经网络模型在CPI预测中的实证检验[J].统计与决策,2017(2):76-78.
[8] 董莉,彭凯越,唐晓彬.大数据背景下的CPI实时预测研究[J].调研世界,2017(8):51-54.
[9] 彭乃驰,党婷.基于小波分析的BP-SARIMA模型的CPI预测[J].统计与决策,2018(16):22-25.
[10] 鲁万波,杨冬.基于半参数混频误差修正模型的中国CPI预测研究[J].统计研究,2018(10):28-43.
[11] 邰晓红,刘义.基于EEMD-PSO-SVM的月度CPI预测研究[J].统计与决策,2019(3):30-33.
[12] Box G E P,Jenkins G M.Time Series Analysis Forecasting and Control-Rev.ed.[J].Journal of Time,1976(4):238-242.
[13] Vapnik V.The Nature of Statistical Learning Theory[M].Springer,1995.
[14] Drucker H,Burges C J C,Kaufman L,Smola A,Vapnik V N.Support Vector Regression Machines[J].Advances in Neural Information Processing Systems,1997(9):155-161.