SAS時間序列分析

1、試驗六時間序列分析一、實驗?zāi)康模簩W(xué)習(xí)時間序列數(shù)據(jù)分析技巧，了解ARIMA模型。二、實驗內(nèi)容：47年1季度到96年3季度美國國民生產(chǎn)總值的季度數(shù)據(jù)。三、實驗要求：寫出分析報告。四、實驗軟件：SAS系統(tǒng)。一般實驗流程：1）平穩(wěn)性檢驗方法：時序圖、自相關(guān)系數(shù)和自相關(guān)圖檢驗、單位根檢驗2）模型識別方法：利用自相關(guān)系數(shù)、偏相關(guān)系數(shù)圖進行模型識別；計算擴展的樣本自相關(guān)函數(shù)并利用其估計值進行模型識別利用最小信息準則進行模型識別；利用典型相關(guān)系數(shù)平方估計值進行模型識別；注：ACF圖和PACF圖的模型識別自相關(guān)系數(shù)圖（ACF圖）偏相關(guān)系數(shù)圖（PACF圖）模型識別結(jié)果q階截尾拖尾MA(q)拖尾P階截尾AR(p)拖

2、尾拖尾ARMA3）模型的參數(shù)估計及檢驗檢驗擬合性、參數(shù)估計顯著性、殘差項無自相關(guān)性（殘差項白噪聲檢驗）4）模型的預(yù)測例題實驗步驟：1)建立數(shù)據(jù)集dataexp3;inputgnp;date=intnx(qtr,1jan47d,_n_-1);formatdateyyqc.;cards;227.8231.7236.1246.3252.6259.9266.8268.1263.0259.5261.2258.9269.6279.3296.9308.4323.2331.1337.9342.3345.3345.9351.7364.2371.0374.5373.7368.7368.4368.7373.4381

3、.9394.8403.1411.4417.8420.5426.0430.8439.2448.1450.1457.2451.7444.4448.6461.8475.0499.0512.0512.5516.9530.3529.2532.2527.3531.8542.4553.2566.3579.0586.9594.1597.7606.8615.3628.2637.5654.5663.4674.3679.9701.2713.9730.4752.6775.6785.2798.6812.5822.2828.2844.7861.2886.5910.8926.0943.6966.3979.9999.3100

4、8.01020.31035.71053.81058.41104.21124.91144.41158.81198.51231.81256.71297.01347.91379.41404.41449.71463.91496.81526.41563.21571.31608.31670.61725.31783.51814.01847.91899.01954.52026.42088.72120.42166.82293.72356.22437.02491.42552.92629.72687.52761.72756.12818.82941.53076.63105.43197.73222.83221.0327

5、0.33287.83323.83388.23501.03596.83700.33824.43911.33975.64022.74100.44158.74238.84306.24376.64399.44455.84508.54573.14655.54731.44845.24914.55013.75105.35217.15329.25423.95501.35557.05681.45767.85796.85813.65849.05904.55959.46016.66138.36212.26281.16390.56458.46512.36584.86684.56773.66876.36977.6706

6、2.27140.57202.47293.47344.37426.67537.57593.6;run;注：Intnx函數(shù)按間隔遞增日期，Intnx函數(shù)計算某個區(qū)間經(jīng)過若干區(qū)間間隔之后的間隔的開始日期或日期時間值，其中開始間隔內(nèi)的一個日期或日期時間值給出。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;3、觀察圖形，發(fā)現(xiàn)圖形成指數(shù)函數(shù)上升形式，故做對數(shù)變換，輸入如下程序:datalexp;setexp3;lgnp=log(gnp);run

7、;4、繪變換后序列圖，輸入如下程序:procgplotdata=lexp;symbol2i二splinec=red;plotlgnp*date=2;run;5、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。對序列做初步識別，輸入如下程序：procarimadata=lexp;identifyvar=lgnpnlag=12;run;運行結(jié)果如下：Figl.DescriptionstatisticsCovarianceCorreIation01.1794741.0000011.1637950.8867121.1479500.9732731.1320510.9597941

8、.1163120.9464551.1Q05C30.9331061.Q84M70.8138271.06910.9065681.0534730.8931791.0373040.57346101.0200140.S654S111.0041770.85138120.3872420.83702CorreIationAutcicorr亡leitions-198765432101234567891HiHadidididiHiihniHidiqlidadiHiqlii|t11iIi11|111111rjii|irplyiTu*BT*T11*1*iT1*T*T*HiHadidididiHiihniHidiqli

9、dadiHiqlii|i町iifT11ini1IT11!11!11111!1IT11!1T1TllTalTllTTl!illalaillillillillillillillillillillillall11illillilliiilai|iiiii1ipu1T11PT1iT11!1T*T|TT|T|T|HiqlidaHiiliHiHiHiHiiliHidi|liHiqJiHiHi|liHii|i|u|ai|iiiiiT1ip11T1111T1iT11!1T1T|TT|T|T|HiHiHiHiillHiiH!111ayim1*1*H1IT*HT1111M*1*T*T1BT*T1*T*HiHadi

10、dididiHiihniHidiqlidadiHii|i11|1111ji1111111j11|ipit1!*T1bT*h11*T*T*T*HiHbdidididiHiihniHidiqlidadii|i町iifT11IiiT1IT11!11!11111!1IT11!1T1TllTaTTIilllaillillillillillillillillillillillall1111iiiiii|iiiii1ipu1T11PT1iT11!1T*T|TT|T|HiqlidaHiiliHiHiHiHiiliHidi|liHiqJiHiHi|lii|i|ii|ai|iiIiiT1H1I1T11I1T1iT

11、11!1T1TIITIT11!1HiHiHiHiillHiiH11111|1111111i|n|ii|n|n|i11iT1T1n-n111HiHadidididiHiihniHidiqlidadi11111111111|1111nI1111iji1111iji|11111HiHadidididiHiihniHidiqlidadii|i町iifT11IiiT1IT11!11!11111!1IT11!1T1TllTaip山中中i|i|ii|ai|iilii1H1U1T11PT1iT11!1T1TIITipmarkstwostandarderrorsInverseAutocorrelations-1

12、98765432101234567891StdError00.0708880.1216960.1559810.1832720.20C3760.2265870.2446320.2809690.2753030.20400.3023640.314180-0.50162-0.001590.00S17-0.004460.002660.001620.00QG2-0.00425-0.001850.00528-0.011140.008G3HiHiillHididiqlida111in|n|ii|ii|ii|ii|ii|ii|nTiFig2.autocorrelations,inverseautocorrela

13、tionsandpartialautocorrelationsAutocorrelationCheckforWhiteNoiseToChi-PrLagSquareDFChiSq61115.61h6.0001122079.3312ChiSqHuiuuurrt=i：i11ui672.57E0.4680.2740.041-0.077-0.223-0.0881285.5212ChiSqriutocorr11tions-60.6510.4193-0.0190.0040.0330.0080.017-0.0371214.2870.0465-0.008-0.0550.1140.1160.162-0.09418

14、23.77130.03330.0010.093-0.0310.1330.0580.1142429.68190.05610.0030.147-0.011-0.019-0.0570.0353035.71250.07620.000-0.022-0.0190.0480.056-0.1093641.10310.10600.02S0.034-0.0400.1230.0070.061模型殘差項的白噪聲檢驗8、提交程序，觀察輸出結(jié)果，可看出模型通過了白噪聲檢驗，說明模型擬合充分。且MA1,3,MA1,4的T值較小，說明參數(shù)顯著為0,除掉這兩項重新進行估計，輸入如下程序:estimateq=(l,2,5)plo

15、t;run;11umiiiiiiiiuIR-I1JCanditionalLeastSquaresEstimationStandsirdApproParametejEstimateErrortValuePrItlLagMU0.017660.001095416.12.0001a-0.46740O.OG613-7.07.0001iMAI,2-0.307150.06592-4.66.00012腳0.300010.061654冷7=1jan96d;run;11、提交程序，并把預(yù)測值記錄下來。實驗練習(xí)：分析武漢市2002/01/01-2003/05/31日火車站旅客客流量數(shù)據(jù)（單位：千人），并預(yù)測6月份前

16、10天的旅客流量。114654911814214815792111110120140936466597377312725445057573030303353626535606357366870666155748560537097957763766845566768857771846471355911080885689657266604251661288569576231838666595180866960584448514954332943110105625355613664615955666656636258605544444050545251444449386932518569896567

17、5651474052777768616475688058585650626260626159595341383650403742475331484651464648776564819277554552618010383817655495460607856615348667458516572767486644051626358645168708285745751305565828977706768789687100111939390103116878264856637557456333851547690747273765685403836467287117100806678667394104931009797655861735238274068825796117653453436366731061147370107110123179107794637575562514753635768667667986810014112011385836136626956587310115714911415462505152466347314955716766786866797737517172687614516815814322522819013319215214