时间序列实验报告实验六9500字

时间序列实验报告实验六9500字

实验六实验名称：拟合残差自回归模型实验目的与要求：掌握用统计软件实现拟合残差自回归模型方法和步骤。实验内容：教材171页的例5.6（续）实验环境与器材：北203机房-minitab统计软件实验过程（步骤）或程序代码：键入数据统计→时间序列→简单→选择c2→确定线性拟合：时间序列→趋势分析→变量实验结果与分析①xt的时间序列图②xt的趋势分析数据xt长度37缺失数据数0拟合趋势方程Yt=66.15+4.52*t准确度度量平均百分误差(MAPE)14.759平均绝对误差(MAD)20.841平均偏差平方和553.812xt的趋势分析图③回归分析:Tt与t回归方程为Tt=66.1+4.52t自变量系数系数标准误TP常量66.1498.1208.150.000t4.51580.372612.120.000S=24.1962R-Sq=80.8%R-Sq（调整）=80.2%方差分析来源自由度SSMSFP回归18601486014146.920.000残差误差3520491585合计36106505异常观测值拟合值标准化观测值tTt拟合值标准误残差残差3737.0279.40233.237.8046.172.02RR表示此观测值含有大的标准化残差④回归分析:xt与xt-1回归方程为xt=18.2+0.927xt-1自变量系数系数标准误TP常量18.1548.2102.210.034xt-10.926580.0531617.430.000S=17.7290R-Sq=89.7%R-Sq（调整）=89.4%方差分析来源自由度SSMSFP回归19550495504303.850.000残差误差3511001314合计36106505异常观测值拟合值标准化观测值xt-1xt拟合值标准误残差残差10100.0018.158.2181.855.21RX37273279.40271.297.448.110.50XR表示此观测值含有大的标准化残差X表示受X值影响很大的观测值。⑤回归分析:xt与xt-1回归方程为xt=1.04xt-1自变量系数系数标准误TP无常数项xt-11.036450.0198652.180.000S=18.6620方差分析来源自由度SSMSFP回归19482389482382722.710.000残差误差3612538348合计37960776异常观测值拟合值标准化观测值xt-1xt拟合值标准误残差残差10100.000.000.00100.005.36R37273279.40283.165.43-3.76-0.21XR表示此观测值含有大的标准化残差X表示受X值影响很大的观测值。⑥自相关函数:残差1滞后ACFTLBQ10.8580735.2229.5120.6497702.5146.9230.4330361.4554.8840.2056150.6556.7350.0018440.0156.736-0.132743-0.4257.557-0.216002-0.6759.798-0.206314-0.6461.919-0.161411-0.4963.2510-0.151776-0.4664.4811-0.126886-0.3865.3812-0.102208-0.3165.9813-0.102679-0.3166.6114-0.118259-0.3567.4915-0.129545-0.3868.5916-0.125660-0.3769.6817-0.097002-0.2970.3618-0.099145-0.2971.1019-0.138576-0.4172.6420-0.177660-0.5275.3221-0.228044-0.6680.0122-0.311090-0.8989.3223-0.378048-1.06104.0524-0.401812-1.10121.97残差1的自相关⑦偏自相关函数:残差1滞后PACFT10.8580735.222-0.328081-2.003-0.110138-0.674-0.198267-1.215-0.071217-0.4360.0627050.387-0.039807-0.2480.2051631.259-0.083272-0.5110-0.231817-1.41110.0715380.4412-0.062013-0.3813-0.006191-0.0414-0.052089-0.32150.0001830.00160.0625630.3817-0.042816-0.2618-0.172466-1.0519-0.145155-0.8820-0.078179-0.4821-0.104789-0.6422-0.188685-1.1523-0.022760-0.14240.0010300.01残差1的偏自相关⑧综合自回归移动平均(ARIMA)模型:残差1每次迭代中的估计值迭代SSE参数014926.60.1000.1000.080112179.30.2500.0320.13329834.90.400-0.0400.17437836.30.550-0.1160.20846153.30.700-0.1940.23654765.50.850-0.2740.25963658.41.000-0.3550.27972820.81.150-0.4370.29782244.31.300-0.5190.31491924.31.450-0.6000.333101857.91.530-0.6370.398111854.01.533-0.6290.506121853.31.530-0.6230.547131853.21.529-0.6210.562141853.21.529-0.6200.566151853.21.529-0.6190.569161853.21.529-0.6190.570171853.21.529-0.6190.571181853.21.529-0.6190.571每个估计值的相对变化不到0.0010参数的最终估计值类型系数系数标准误TPAR11.52870.138611.030.000AR2-0.61890.1434-4.320.000常量0.5711.2660.450.655平均值6.3314.03观测值个数:37残差:SS=1818.08（不包括向后预测）MS=53.47DF=34修正Box-Pierce(Ljung-Box)卡方统计量滞后12243648卡方12.923.833.5*自由度92133*P值0.1670.3050.444*⑨综合自回归移动平均(ARIMA)模型:残差1每次迭代中的估计值（模型2）迭代SSE参数014929.90.1000.100112196.10.2500.03229859.90.400-0.04137866.10.550-0.11746185.50.700-0.19654798.40.850-0.27663690.51.000-0.35872850.51.150-0.44082270.21.300-0.52491944.01.450-0.606101870.41.539-0.649111867.31.544-0.644121866.91.543-0.640131866.81.543-0.638141866.81.543-0.637151866.81.543-0.637每个估计值的相对变化不到0.0010参数的最终估计值类型系数系数标准误TPAR11.54260.135911.350.000AR2-0.63720.1409-4.520.000观测值个数:37残差:SS=1811.38（不包括向后预测）MS=51.75DF=35修正Box-Pierce(Ljung-Box)卡方统计量滞后12243648卡方13.324.034.0*自由度102234*P值0.2090.3460.466*⑩xt,xt的拟合值的时间序列图自相关函数:at滞后ACFTLBQ1-0.034928-0.210.052-0.129209-0.780.7430.1704391.021.974-0.205781-1.203.8250.0825330.464.1360.0231970.134.157-0.267176-1.497.5980.1417990.758.5990.2433331.2611.6410-0.169548-0.8413.1811-0.038495-0.1913.2612-0.008613-0.0413.2613-0.045873-0.2213.3914-0.072364-0.3513.7215-0.054325-0.2613.9116-0.072976-0.3514.28170.0823060.4014.77180.1474610.7116.4219-0.158618-0.7518.43200.0213690.1018.47210.1351490.6320.1222-0.171065-0.7922.9423-0.011875-0.0522.9524-0.098278-0.4524.02at的自相关①自相关函数:残差2滞后ACFTLBQ10.0810820.490.2620.0049660.030.2630.0278600.170.304-0.055917-0.340.435-0.046201-0.280.536-0.071956-0.430.777-0.228393-1.373.288-0.141247-0.804.2790.0205580.124.30100.0055330.034.30110.0510500.294.44120.0631930.354.67130.0303420.174.73140.0069470.044.7315-0.038367-0.214.8316-0.052168-0.295.01170.0032470.025.01180.0660200.375.3519-0.007259-0.045.3520-0.037869-0.215.47210.0603620.335.8022-0.044789-0.255.9923-0.080607-0.446.6624-0.142030-0.788.90残差2的自相关②自相关函数:残差2_1滞后ACFTLBQ10.4661362.808.4920.0950260.488.863-0.042956-0.218.934-0.312987-1.5613.125-0.223632-1.0415.336-0.216898-0.9817.477-0.199177-0.8819.3480.1117420.4819.9590.1922990.8321.83100.0048970.0221.8311-0.012791-0.0521.8412-0.044881-0.1921.9513-0.105238-0.4422.6114-0.126599-0.5323.6115-0.072151-0.3023.95160.0549500.2324.15170.2116690.8827.38180.2505371.0232.15190.1144410.4533.20200.1247470.4934.53210.0593930.2334.8622-0.180633-0.7038.0423-0.230868-0.8943.6524-0.278502-1.0552.50残差2_1的自相关③自相关函数:残差2_1滞后PACFT10.4661362.802-0.156196-0.943-0.027973-0.174-0.345435-2.0750.1170180.706-0.247196-1.4870.0026370.0280.1657220.9990.0307270.1810-0.280607-1.68110.0343210.21120.0222090.1313-0.078825-0.4714-0.187468-1.12150.2115761.27160.0099920.06170.0535710.32180.0664720.40190.0576360.35200.0789160.4721-0.028220-0.1722-0.120558-0.72230.0004630.0024-0.170539-1.02残差2_1的偏自相关④综合自回归移动平均(ARIMA)模型:残差2_1每次迭代中的估计值（模型3）迭代SSE参数02326.070.10012103.100.25021993.510.40031981.200.46641981.160.47051981.160.470每个估计值的相对变化不到0.0010参数的最终估计值类型系数系数标准误TPAR10.47000.14973.140.003观测值个数:36残差:SS=1980.44（不包括向后预测）MS=56.58DF=35修正Box-Pierce(Ljung-Box)卡方统计量滞后12243648卡方13.728.8**自由度1123**P值0.2490.187**⑤综合自回归移动平均(ARIMA)模型:xt/r/