SAS时间序列分析

1、试验六时间序列分析一、实验目的：学习时间序列数据分析技巧，了解ARIMA模型。二、实验内容：47年1季度到96年3季度美国国民生产总值的季度数据。三、实验要求：写出分析报告。四、实验软件：SAS系统。一般实验流程：1）平稳性检验方法：时序图、自相关系数和自相关图检验、单位根检验2）模型识别方法：利用自相关系数、偏相关系数图进行模型识别；计算扩展的样本自相关函数并利用其估计值进行模型识别;利用最小信息准则进行模型识别；利用典型相关系数平方估计值进行模型识别；注：ACF图和PACE3的模型识别自相关系数图（ACF图）偏相关系数图（PACES）模型识别结果q阶截尾拖尾FMA(q)拖尾P阶截尾AR(p

2、)拖尾拖尾ARMA3）模型的参数估计及检验检验拟合性、参数估计显著性、残差项无自相关性（残差项白噪声检验）4）模型的预测例题实验步骤：1）建立数据集dataexp3;inputgnp;date=intnx（qtr,1jan47d,_n_-1）;formatdateyyqc.;cards;227.8231.7236.1246.3252.6259.9266.8268.1263.0259.5 261.2258.9269.6279.3296.9308.4323.2331.1337.9 342.3345.3345.9351.7364.2371.0374.5373.7368.7417.8368.4368.

3、7373.4381.9394.8403.1411.4420.5 426.0430.8439.2448.1450.1457.2451.7444.4448.6461.8475.0499.0512.0512.5516.9530.3529.2532.2527.3531.8542.4553.2566.3579.0586.9594.1597.7606.8615.3628.2637.5654.5663.4674.3679.9701.2713.9730.4752.6775.6785.2798.6812.5822.2828.2844.7861.2886.5910.8926.0943.6966.3979.9999

4、.31008.01020.31035.71053.81058.41104.21124.91144.41158.81198.51231.81256.71297.01347.91379.41404.41449.71463.91496.81526.41563.21571.31608.31670.61725.31783.51814.01847.91899.01954.52026.42088.72120.42166.82293.72356.22437.02491.42552.92629.72687.52761.72756.12818.82941.53076.63105.43197.73222.83221

5、.03270.33287.83323.83388.23501.03596.83700.33824.43911.33975.64022.74100.44158.74238.84306.24376.64399.44455.84508.54573.14655.54731.44845.24914.55013.75105.35217.15329.25423.95501.35557.05681.45767.85796.85813.65849.05904.55959.46016.66138.36212.26281.16390.56458.46512.36584.86684.56773.66876.36977

6、.67062.27140.57202.47293.47344.37426.67537.57593.6；run;注：Intnx函数按间隔递增日期，Intnx函数计算某个区间经过若干区间间隔之后的间隔的开始日期或日期时间值，其中开始间隔内的一个日期或日期时间值给出。Intnx函数的格式如下：Intnx(interval,from,n)2、2）绘序列图，输入如下程序:procgplotdata=exp3;symbol1i=spline;plotgnp*date=1;run;datalexp;setexp3;lgnp=log(gnp);run;4、绘变换后序列图，输入如下程序:procgplotdat

7、a=lexp;symbol2i=splinec=red;plotlgnp*date=2;run;1945:11950:11956:119S0:119GB:11970:1197B:11980:113a8:11930:1199E:1200D:1date5、提交程序，到graph窗口中观察变换后的序列图，可以看出它成直线上升趋势。对序列做初步识别，输入如下程序：procarimadata=lexp;identifyvar=lgnpnlag=12;run;运行结果如下：TheSASSystem21:51Tuesday,DecemberS,0111TheARINAProcedureNusofVariab

8、le=ImpMeanofWorkingSeries7JS4182StandardDeviationL086036NumberofObservations199Fig1.DescriptionstatisticsAutocorrelaiionsCovarianceCorrelation1J734741J637951.147950M32O51L1163121.1005的1.0849071.顺的1.0534731.0373041.0209141.0041770.S872421.000000.986710.973270.959790.94E450.933100.919620.906560.893170

9、.8794S0.S65430.851380.88702-198754321012345676S1HkHjMjall.dj山咨命山山山山中山山-沙iqn|i|i甲11ri|if1if11fiigiijiiTqiaT,甲1TlI3 小油山山山山山山UlidUdUWW山41小心必Ijirpuqit|ii|ii|ii|trjaipnpii|ii|iiirjiiquyij|ii|ii|ii|iJliJlJldl-JidrdjBjfHflUlkilaiirilijlJl119111-di-drw35如*必匕击皿曲出如如心必面不邛帮牛不单学甲甲尊面骑单用不邙币不IHiHrHoX山XEXi1X1411X19X

10、AXB咿甲叩津甲币币不值币 E 币不遇不lull)：IJJiJ.niJIX|X|X|X|Xa|J.|lXL|llX|山山.F 印用甲币币不不小甲而而旧于不不加赤-福Jjijjijjj山如一山一心ijjij11nxnXnjjJJIJIJIJJ山tli如,Eqpii|rqirqlrqii_f|j.Jjsli山，!，L，L，L惧1L1如1立,，L*L山WWW山山,山山击山山山的WWW山讪心，in|i1171Tin|iiji：i|viTar|rpiTir|iiT|CrilT|,1T|aTl:lTlIIIididiahah-drdadaHfiibIII!diKhaliah.F 不叩市举邢4sHiGEEm

11、面不咿叩叩Iliillrji：iIill11HI1IiiIululiiliiInI!1i.印邙尊邛叩叩隼不不平不液罩印不邛咿必需曾曾如山:如如由!41山WW佻5W业.ii|lOn*H*lifrrprprprpf|1r|liqln*511merkstwo5tendarderrorsInverseAutoccrnelationsLagCorrelation1234?89101112-0.50162-0.001590.00617-0.004480.002660.00162口 。除-0.00425-0.001850.00528-0.011140.00963ihHjJiillikIKIHHIINIIII

12、111r11|1iflifl1|11Sl|1If111PartialAutocorrehtionsLeg1789101112Correldticn0.38671-0.01202-0.00853-0.00173-0.00720-0.00396-0*00530-D.01211-0.01892-0.01748-0.01213-0.01723StdError00.0708880.12169S0.155刈0J83272Q.20G3760,2265870.2448320.2809690.2758030.2896480.3023640.314180Fig2.autocorrelations,inversea

13、utocorrelationsandpartialautocorrelationsLag0123466189101112Fig3.autocorrelationcheckforwhitenoise6、提交程序，观察样本自相关系数，可看出有缓慢下降趋势，结合我们观察的图形，我们知道要对序列做差分运算，作一阶差分，输入如下程序：identifyvar=lgnp(1)nlag=12;run;结果如下:AutocarreIationsLagSquareIXDFChiSq-ftutocorre1atli6111506,0001D.湖0.878OGiO0,9460.983122079.3312049200

14、.037LacCovarianceCorrelatIOT-1937c0.000150101,000001。 .00的7。0.4082020.000041120.2739536J817E-80,040654-0.000011550.00003S5-.22907e-0.00Q0132-.087657-2.7533E-6-.0183882.0931E-60.019809O.OOOQ24950.16825100.000031620.2106511Q.000贯&0.1897?122.8S222E-60.019204321(112345678913tdErroriLs11ilHili11illr1111l

15、uXA1I1,lrilnlul,IXIii|i|iipipqii|i|iupiji111i|i11|i|iin|i|i010.0710G7率Hi*申0.0052840.0095700.089664MW0.0838940.0927450.0931640.093182“明0.093193I常帆容0.094679I0.0870170.098874ndarderrorsmarkstwo4InverseAutocorrelatiorsLSLCorrelatior-I987654321C1234567891101112-0.31993-0.135780.03457-0.049730.21733-0.1B2

16、30-O.026B20.19490-0,05311-0.02G89-0.121410.10534.9M:*确*Hf:索 .涉*H41褊aThe蝴I岫ProcedurePartiedAutoccrrelationsAutocorreKtionCheckforWhiteNoise/itocorrelat后愤0,460.2740.041-0,0?7-0,223-0.0a叩J18。5140.1耻D,211D,1900.0197、提交程序，观察样本自相关系数，可看出样本自相关系数5步后是截尾的，那么确定为MA(5)模型，进行参数估计，输入如下程序estimateq=5plot;run;结果如图:rhl-

17、r1-r*-inrrir-wvivConditionalLeastSquaresEstimationParameterEstimateStandardErrortValueApproxPr|tlLagMU0.017650.001225214.40.QQQ10MA1J-0,48763O.OG943-7,02,00011MM,2*0,350130.07785-4.60.00012-0.013300.06130-0.920435843NAI,4-0碌0.07797-0,260J9334MALE0.284410.089494.IQLasSquareDFChiSq72,576,00011295.5212

18、UHIIHUILUUUBrC1aL1Ulis60.S&10.4193-0.0190,0040.0930.0030LagSquaredDFChiSq-rutocofrelations1模型残差项的白噪声检验RutocorrelationPlotofResidualsLagCovarianceCorrelaticn-198765432101234557891StdErrorQ0.00010323U000001i_Liiyj1.：Li( (LiIjuIjjjIjjjI史yMUIJJifiIT1T1T1T1,T1T1T1T1T1r1TJ1IT1014J2891E*70,003780,07106724.

19、27371E-G0.03S12*o.onosa39J358IE-6。,。8912的.0*0711774-6J872E-7006210.07173652.31551E-604川相0.0717418-2.5301E-6-.023160.07177271J4975E-70,001240,0718108-2.85E-6-.02603:*0.07101090.00DQ11330J0376神：0,071858100.000013280.121即榔.0J72611110.000018880J7267楙*0407363212-3J03IE-6二07967l0*075654markstwostandarderr

20、ors残差项的自相关系数图9、提交程序，观察输出结果，可看出模型通过了白噪声检验，说明模型拟合充分，且残差标准误与前一估计相差很小，故以此结果为我们所要的结果，依此结果写出方程式。NovingAverageFactorsgictor1:1+0.4674+0.30715日愀C2)-0.3QD01St&ndardApproxParametejrEstimateErrortValuePr|t|LagMU017旗Q.00109E4IC.120001CMALI-0,4S7400.06S13-7.07.00011MAIL2-0.307150.08692-4.66*00012略1,30.3Q0010.061

21、854.87=1jan96d;run;11、提交程序，并把预测值记录下来。21:51TuesdayTDewmber6i201126TheARIMAProcedureForecastsforVariableIgnpQbsForecast8tdError95%ConfidenceLiKiiU2006.94320.0105e277乱2018.965。0.01S68.3?册9.00132028.昵$70.02628.98229.03512039.00130.OS2I9.0C432049.J2290.03718.94989.89502059.08890.04028.96123.1187ThtSASSys

22、tem实验练习：分析武汉市2002/01/01-2003/05/31日火车站旅客客流量数据（单位：千人），并预测6月份前10天的旅客流量。11465491181421481579211111012014093646659737731272544505757303030335362653560635736687066615574856053709795776376684556676885777184647135591108088568965726660425166128856957623183866659518086696058444851495433294311010562535561366461595566665663625860554444405054525144444938693251856989656756514740527777686164756880585856506262606261595953413836504037424