时间序列实验报告(相关系数)

1、时间序列分析实验报告实验课程名称实验项目名称AR,MA模型的自相关与偏相关分析年级专业学生姓名成绩 理学院实验时间：2015年11月17日学生所在学院：理学院专业：应用数学班级：数学班姓名孙晗学号115113001152实验组实验时间2015，11，17指导教师谢建春实验项目名称AR，MA及其相关系数实验目的及要求：熟悉时间序列中符合AR,MA模型的建模原理掌握时间序列中符合AR,MA模型的数据，自相关系数与偏相关系数所具有的特征实验原理：对于一组历史数据对未来的发展有一定影响的平稳序列，通过AR，MA模型的建模，可以挖掘历史数据中的有效信息，用来预测序列未来的发展。实验硬件及软件平台：计算机

2、Eview网络实验步骤：1、在Eview中录入数据2、绘制它的时序图，自相关图，偏自相关图。3、根据自相关系数和偏自相关系数，选择阶数适当的AR,MA模型进行拟合。4、估计模型中未知参数的值5、检验模型的有效性6、模型优化7、利用拟合模型，预测序列的将来走势实验内容（包括实验具体内容、算法分析、源代码等等）:某城市过去63中每年降雪量数据（单位mm）如下表所示126.482.47S.151.190.976.2104.587.4110.52569.353.539.063.646.772.979.603.680.760.37974.449.654.771.84=9.1103.951.682.4S3

3、.677.S79.3S9.685.55S120.7110.565.4：39.940.1S8.771.48355.989.9S4.S105.2113.7124.7114.5115.6102.4：101.4S9.S71.570.990.355.566.170.4120.5971101、判断该序列的稳定性和纯随机性该序列的时序图如下：(dUGraph:UWTTLEDWorkfireUMTTTLED:LrntitbdX_nXViewProcObjectPrintNameFreezeOptionsUpdateAddTextLine/ShadeRemoY由时序图显示没有明显的趋势或者周期，基本可以看成是平

4、稳序列。做如下自相关图:除了延迟1-2阶的自相关系数在2倍的标准差范围之外，其他阶数的自相关系数都在2倍的标准差范围之内波动。根据自相关系数的这个特点可以判断该序列具有短期相关性。进一步确定序列平稳。2、选择阶数适当的AR,MA模型进行拟合AR(1)冃Equation:inVTTTLEDWorkfile:UNTITLED:Untitled_nx|ViewProcObject|PrintNameFreeze|EstimateForecastStatsResidsependlentVariable:Method:LeastSquaresate:11/28/15Time:22:55Sample(ad

5、justed):263Includedobservations:62afteradjustmentsConvergenceachievedafter3iterationsVariableCoefficientStd.Errort-StatisticProb.c79.430774.09478019.398060.0000AR(1)0.3135780.1200352.6123970.0113R-squared0.102127Meandependentvar79.55161AdjustedR-squared0.087163S.D.dependentvar23.16126S.E.ofregressio

6、n22.12886Akaikeinfocriterion9.063368Sumsqua.redresid29381.17Schwarzcriterion9.131985Loglikelihood-278.9644Hannan-Qiiinncriter.9.090309F-statistic6.824616Durbin-Watsonstat2.088033Prob(F-statiStic)0.011345InvertedARRoots.31AR(2)回Equation:UNTTLEDWorldfle:UNmLED:Untitled_BX|ViewProcObjectPrintNameFreeze

7、EstimateForecastStatsResidsDependentVariable:YMethod:LeastSquaresate:11/23/15Time:22:54Sample(adjusted):363Includedobservations:61afteradjustmentsConvergenceachievedafter3iterationsVariableCoefficientStd.Errort-StatisticProb.c79.491964.12601919.266020.0000AR(2)0.3052080.1219902.5019110.0151R-squared

8、0.095918Meandependentvar79.50492AdjustedR-squared0.080594S.D.dependentvar23.35053S.E.ofregression22.33981Akaikeinfocriterion9.007326Sumsquaredresid29576.91Schwarzcriterion9.156535Loglikelihood-275.1635Hannan-Cluinncriter.9.114450F-statistic6.259558urbin-Watsonstat1.441296ProbfF-statistic)0.015145Inv

9、ertedARRoots.55-.55MA(1)目Equation:UNTTLEDWor好咤inVTTTLED:Untitled_XViewProcObjectPrintNameFreezeEstimateForecastStatsResidsDependentVariable:YMethod:LeastSquaresate:11/28/15Time:22:53Sample:163Includedobservations:63Convergenceachievedafter15iterationsMABackcast:0VariableCoefficientStd.Errort-Statist

10、icProb.c80.509933.54300622.723620.0000MA(1)0.2211530.1256001.7607730.0833R-squared0.068012Meandependentvar80.29524AdljustedlR-squaredl0.052734S.D.dlependlentvar23.71981S.E.ofregression2308593Akaikeinfocriterion9.147555Sumsquared!resid32510.56Schwaizcriterion9.215591Loglikelihood-286.1480Hannan-Quinn

11、criter.9.174314F-statistic4.451487Durbin-Watsonstat1.830958Prob(F-statiStic)0.038981InvertedMARoots-.22MA(2)回Equation:UNTITLEDWorkfileUNTTrLEO:Untitled_HXViewProcObjectPrintNameFreezeEstimateForecastStatsResidsDependentVariable:YMethod:LeastSquaresDate:11/28/15Time:22:51Sample:163Includedobservation

12、s:63Convergenceachieved!after5iterationsMABackcast:-10VariableCoefficientStd.Errort-StatisticProb.C80.604113.70723121.742400.0000MA(2)0.2951570.1210122.4390630.0177R-squaredl0.086874Meandlependlentvar80.29524AdljustedlR-squaredl0.071905S.D.dlependlentvar23.71981S.E.ofregression22.85112Akaikeinfocriterion9.127109Sumsquared!resid31852.