SAS時(shí)間序列分析

1、試驗(yàn)六時(shí)間序列分析一、實(shí)驗(yàn)?zāi)康模簩W(xué)習(xí)時(shí)間序列數(shù)據(jù)分析技巧，了解ARIMA模型。二、實(shí)驗(yàn)內(nèi)容：47年1季度到96年3季度美國(guó)國(guó)民生產(chǎn)總值的季度數(shù)據(jù)。三、實(shí)驗(yàn)要求：寫(xiě)出分析報(bào)告。四、實(shí)驗(yàn)軟件：SAS系統(tǒng)。一般實(shí)驗(yàn)流程：1）平穩(wěn)性檢驗(yàn)方法：時(shí)序圖、自相關(guān)系數(shù)和自相關(guān)圖檢驗(yàn)、單位根檢驗(yàn)2）模型識(shí)別方法：利用自相關(guān)系數(shù)、偏相關(guān)系數(shù)圖進(jìn)行模型識(shí)別；計(jì)算擴(kuò)展的樣本自相關(guān)函數(shù)并利用其估計(jì)值進(jìn)行模型識(shí)別;利用最小信息準(zhǔn)則進(jìn)行模型識(shí)別；利用典型相關(guān)系數(shù)平方估計(jì)值進(jìn)行模型識(shí)別；注：ACF圖和PACE3的模型識(shí)別自相關(guān)系數(shù)圖（ACF圖）偏相關(guān)系數(shù)圖（PACES）模型識(shí)別結(jié)果q階截尾拖尾FMA(q)拖尾P階截尾AR(p

2、)拖尾拖尾ARMA3）模型的參數(shù)估計(jì)及檢驗(yàn)檢驗(yàn)擬合性、參數(shù)估計(jì)顯著性、殘差項(xiàng)無(wú)自相關(guān)性（殘差項(xiàng)白噪聲檢驗(yàn)）4）模型的預(yù)測(cè)例題實(shí)驗(yàn)步驟：1）建立數(shù)據(jù)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函數(shù)按間隔遞增日期，Intnx函數(shù)計(jì)算某個(gè)區(qū)間經(jīng)過(guò)若干區(qū)間間隔之后的間隔的開(kāi)始日期或日期時(shí)間值，其中開(kāi)始間隔內(nèi)的一個(gè)日期或日期時(shí)間值給出。Intnx函數(shù)的格式如下：Intnx(interval,from,n)2、2）繪序列圖，輸入如下程序:procgplotdata=exp3;symbol1i=spline;plotgnp*date=1;run;datalexp;setexp3;lgnp=log(gnp);run;4、繪變換后序列圖，輸入如下程序: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窗口中觀察變換后的序列圖，可以看出它成直線上升趨勢(shì)。對(duì)序列做初步識(shí)別，輸入如下程序：procarimadata=lexp;identifyvar=lgnpnlag=12;run;運(yùn)行結(jié)果如下：TheSASSystem21:51Tuesday,DecemberS,0111TheARINAProcedureNusofVariab

8、le=ImpMeanofWorkingSeries7JS4182StandardDeviationL086036NumberofObservations199Fig1.DescriptionstatisticsAutocorrelaiionsCovarianceCorrelation1J734741J637951.147950M32O51L1163121.1005的1.0849071.順的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*必匕擊皿曲出如如心必面不邛幫牛不單學(xué)甲甲尊面騎單用不邙幣不IHiHrHoX山XEXi1X1411X19X

10、AXB咿甲叩津甲幣幣不值幣 E 幣不遇不lull)：IJJiJ.niJIX|X|X|X|Xa|J.|lXL|llX|山山.F 印用甲幣幣不不小甲而而舊于不不加赤-福Jjijjijjj山如一山一心ijjij11nxnXnjjJJIJIJIJJ山tli如,Eqpii|rqirqlrqii_f|j.Jjsli山，!，L，L，L懼1L1如1立,，L*L山WWW山山,山山擊山山山的WWW山訕心，in|i1171Tin|iiji：i|viTar|rpiTir|iiT|CrilT|,1T|aTl:lTlIIIididiahah-drdadaHfiibIII!diKhaliah.F 不叩市舉邢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業(yè).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，觀察樣本自相關(guān)系數(shù)，可看出有緩慢下降趨勢(shì)，結(jié)合我們觀察的圖形，我們知道要對(duì)序列做差分運(yùn)算，作一階差分，輸入如下程序：identifyvar=lgnp(1)nlag=12;run;結(jié)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貫&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:*確*Hf:索 .涉*H41褊aThe蝴I岫ProcedurePartiedAutoccrrelationsAutocorreKtionCheckforWhiteNoise/itocorrelat后憤0,460.2740.041-0,0?7-0,223-0.0a叩J18。5140.1恥D,211D,1900.0197、提交程序，觀察樣本自相關(guān)系數(shù)，可看出樣本自相關(guān)系數(shù)5步后是截尾的，那么確定為MA(5)模型，進(jìn)行參數(shù)估計(jì)，輸入如下程序estimateq=5plot;run;結(jié)果如圖: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模型殘差項(xiàng)的白噪聲檢驗(yàn)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殘差項(xiàng)的自相關(guān)系數(shù)圖9、提交程序，觀察輸出結(jié)果，可看出模型通過(guò)了白噪聲檢驗(yàn)，說(shuō)明模型擬合充分，且殘差標(biāo)準(zhǔn)誤與前一估計(jì)相差很小，故以此結(jié)果為我們所要的結(jié)果，依此結(jié)果寫(xiě)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，并把預(yù)測(cè)值記錄下來(lái)。21:51TuesdayTDewmber6i201126TheARIMAProcedureForecastsforVariableIgnpQbsForecast8tdError95%ConfidenceLiKiiU2006.94320.0105e277亂2018.965。0.01S68.3?冊(cè)9.00132028.昵$70.02628.98229.03512039.00130.OS2I9.0C432049.J2290.03718.94989.89502059.08890.04028.96123.1187ThtSASSys

22、tem實(shí)驗(yàn)練習(xí)：分析武漢市2002/01/01-2003/05/31日火車(chē)站旅客客流量數(shù)據(jù)（單位：千人），并預(yù)測(cè)6月份前10天的旅客流量。11465491181421481579211111012014093646659737731272544505757303030335362653560635736687066615574856053709795776376684556676885777184647135591108088568965726660425166128856957623183866659518086696058444851495433294311010562535561366461595566665663625860554444405054525144444938693251856989656756514740527777686164756880585856506262606261595953413836504037424