SPSS时间序列分析案例

1、用SPSS软件做时间序列分析，有某公司2002年一季度到2010年二季度的34个税后利润 数据，要求预测出该公司2010年三季度和四季度的税后利润。要求：1. 画出序列趋势图2. 绘制出自相关图和偏自相关图3. 确定参数和模型4. 给出预测值观测值序列图税后盈利自相关图序列:税后盈利滞后自相关标准误差。Box-Ljung统计量值dfSig.b1.306643.4821.0622】98624.9872.083385.1596.3403.0964.5425718.3424.0015.084.15418.6415.0026.067.15118.8366.0047.0944919.2397.0078.

2、4584629.0938.0009.0414329.1769.00110.0164029.18910.00111.0123729.19711.00212.2363432.30812.00113-.0923132.80613.00214-.0942833.34514.00315-.0792533.74515.00416062134.51016.005a. 假定的基础过程是独立性（白噪音）。b. 基于渐近卡方近似。税后盈利系数置信上限直信下礙o o O鉅迟数II偽自相关序列:税后盈利滞后偏自相关标准误差1.306712.11571307714.503715-.279716-.010717.0467

3、18.268719J307110-.0547111-.0537112-.0817113-.0407114-.0517115-.0277116-.06271税后盈利dOVd10 11 12 13 14 15 16系数置倍上限 设佶下礙(r1O.5-O-13、确定参数和模型时间序列建模程序模型描述模型类型模型ID 税后利润模型ARIMA(O,EO)(O,bO)榛型摘要必聖紅合g如mSE昱刘B51025507$909$干症的R方5 5O2E-1 7$ 5O2E-17s.sozE-irS5O2E-17S5O2E-175 5O2E-175.S02E-175.502E-1T5.5O2E-175.502E-

4、17R方.831.831.831.831.831.8R.831.831.831.931RMSG10472 30310472 3031 0472.30310472 30310472 30310472 JO310472.3031 0V2.30310472 30310472 303rwPE23.20923.20923.20923.20923.20923.2523.20923.20923.20923.209M50APE148.390148.390146.390148.390148.390140.3W148.390148.390148.39014B.380ME7iO9 8f57509 8157509.8

5、157509.8157609 8157509.8157509.8167509.8157&J9 8I57509.815M«<AE2 翊 51"27835.17727935.11127935.17727935.17727935.17727935.11127935.11127036.127935.17ZBIC16.82918.82919.62916 629168291B.82919.62919.6291682918 G29模型统计量模型预测变呈数模型拟合统计童Ljung-BoxQ(18)离群值数平稳的R方统计量DFSig.税后利润模型05.5O2E-1717.688

6、18.4760200000.00-观剧位拟合值预测150000.00-100000.00-50000.00-II I III IIII I I IIII IQ1Q3 Q1Q3Q1Q3 Q1Q3Q1Q3 Q1Q3 Q1Q3Q1Q3 Q1Q3200220022003200320042004200520052006200620072007200820082009200920102010口期4、给出预测值2010年第三季度139621.02万元2010年第四季度170144.55万元剔除季节成分后，平滑处理及剔除循环波动因素的序列图SEASON MOD 2 MUL EQU 4中税麻利润象节性调SEAS

7、ONx MOD_6s MUL. EQU、4中税后利润的季节性调整序列自相关图序列:SEASON、MOD 6、MUL、EQU、4中税后利润的季节性调整序列滞后自相关标准误差。Box-Ljung统计量值dfSig?1.7286419.6331.0002.4506227.3832.0003.3105931693.0004.2075732.9114.0005.2195434.9415.0006.241.15137.4846.0007.2434940.1687.0008.2264642.5718.0009834344.2139.00010624045.55110.00011.093.13746.0121

8、1.00012.0063446.01512.00013-.047.13146.14513.00014-.0212846.17214.00015-.0222546.20415.00016-.0362146.29416.000O假定的基础过程是独立性（白噪音）。b.基于渐近卡方近似。SEASON> M0D_6、MUL. EQU> 4屮税麻利润的李节性调整序列系数疋信下垠延迟数II偽自相关序列:SEASON、MOD_6、MUL、EQU、4中税后利润的季节性调整序列滞后偏自相关标准误差1.728712J687108-.053.206.000.076-.015.014.034-.121-.0

9、66-.05915-.134.019345678910111213141516SEASON. WIOD_6、MUL. EQU、4中税后利润的李节性调整序列12345678910 11 12 13 14 15 16薙迟数U樓型描述槿型类型模型 ID SEASON MOD_6、MUL、模型JEQU、4中税后利润的季节性调整序列ARIMA (0)0)(000)蛀搦合拟合缭惬百分应均值SE杲小值510QS50759096平稻的R方-2.691E-16-2.691 E-16-2 591E-162.591 E-16-2.691 E-16-2 591 E-162 591 E-16-2.591 E-16-2

10、S91 E-16-Q 591 E-16R方.812.812.812.Bl 2.812.312.B12.812.812.B12RMSE10075.0961 D075.09610075.0961005.09610075.09 &10075.096100?5.D9610075.03610075.096100F5.096MAPE18.15418.15418.1541815418.15410.1541815418.15418.15418154“aPE65 07255.07255.07255.0T255.07255.07255.0T255.07255.07255.0T2MAE7584.762758

11、4.7627534.76275B4.7627584.762753476275B4.7627584.7627584.7627584762MaE35041 64935041.64935041.64935041 64935041.64935041.64935041 64935041.64935041.64936041 64918.54218.54218.54218.54218.54218.54218.54218.54218.54218 542樓型统计壬观测值拟合值预观-03-Q3 © -03 © -03 © -Q3 © -03 © -Q3 © -Q3 0220 220 2009 2009 2008 2008 2007 2007 2006 2006 2005 2005 2004