基于ARIMA与数据累加生成的区间时间序列混合预测模型
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摘要
为了提高区间时间序列的模型预测精度,提出一种改进ARIMA模型的方法。将二元与三元区间序列分别转换为含有等量信息的实数序列,结合灰色模型中的数据累加处理方法和ARIMA模型实现实数序列建模,还原处理得到区间预测序列。数据分析表明,当区间序列波动较小时,不进行数据累加处理就能得到较高精度的区间预测序列,而当区间序列波动较大时,数据累加处理方法消除了原数据的随机性,更好地挖掘了建模序列的规律,因而得到更高精度的预测序列。
In order to improve the accuracy of the interval time series in forecasting model,an improved ARIMA model is proposed.The binary and ternary interval time series are changed into real sequences which contain the equivalent information,and then the cumulative processing method of the gray model is combined with ARIMA model to realize real sequence prediction.Finally the interval prediction can be obtained through the restoring procedure.The data analysis shows that the interval prediction sequence with high precision can be got without the data accumulated processing when the fluctuation of the interval sequence is small.But when the fluctuation of the interval sequence is large,the processing method of data accumulation eliminates the randomness of the original series and can be better to learn about the regular pattern of modeling sequence,so that the prediction sequence with higher precision can be got.
In order to improve the accuracy of the interval time series in forecasting model,an improved ARIMA model is proposed.The binary and ternary interval time series are changed into real sequences which contain the equivalent information,and then the cumulative processing method of the gray model is combined with ARIMA model to realize real sequence prediction.Finally the interval prediction can be obtained through the restoring procedure.The data analysis shows that the interval prediction sequence with high precision can be got without the data accumulated processing when the fluctuation of the interval sequence is small.But when the fluctuation of the interval sequence is large,the processing method of data accumulation eliminates the randomness of the original series and can be better to learn about the regular pattern of modeling sequence,so that the prediction sequence with higher precision can be got.
引文
[1]岳继光,杨臻明,孙强,等.区间时间序列的混合预测模型[J].控制与决策,2013(12):1915-1920.
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[5]曾祥艳,舒兰.基于灰模型的区间模糊数时间序列预测[J].数学的实践与认识,2015,45(5):55-62.
[6]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002,2:210-252.
[7]BOX G,JENKINS G.Time Series Analysis:Forecasting and Control[M].San Francisco:Holden Day,1976:28-32.
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[9]吉培荣,胡翔勇,熊冬青.对灰色预测模型的分析与评价[J].水电能源科学,1999,6(2):42-43.
[2]宋中民.灰色区间预测的新方法[J].武汉理工大学学报(交通科学与工程版),2002,26(6):796-799.
[3]肖枝洪,郭明月.时间序列分析与SAS应用[M].武昌:武汉大学出版社,2009:145-163.
[4]聂淑媛.时间序列分析的历史发展[J].广西民族大学学报(自然科学版),2012,18(1):24-28.
[5]曾祥艳,舒兰.基于灰模型的区间模糊数时间序列预测[J].数学的实践与认识,2015,45(5):55-62.
[6]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002,2:210-252.
[7]BOX G,JENKINS G.Time Series Analysis:Forecasting and Control[M].San Francisco:Holden Day,1976:28-32.
[8]夏丽.基于ARIMA模型及回归分析的区域用电量预测方法研究[D].南京:南京理工大学,2013:2.
[9]吉培荣,胡翔勇,熊冬青.对灰色预测模型的分析与评价[J].水电能源科学,1999,6(2):42-43.