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PACF图的模型识别自相关系数图（ACF图）偏相关系数图（PACF图）模型识别结果q阶截尾拖尾MA(q)拖尾P阶截尾AR(p)拖

2、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;227.8231.7236.1246.3252.6259.9266.8268.1263.0259.5261.2258.9269.6279.3296.9308.4323.2331.1337.9342.3345.3345.9351.7364.2371.0374.5373.7368.7368.4368.7373.4381

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;3、观察图形，发现图形成指数函数上升形式，故做对数变换，输入如下程序:datalexp;setexp3;lgnp=log(gnp);run

7、;4、绘变换后序列图，输入如下程序: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：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模型残差项的白噪声检验8、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MA1,3,MA1,4的T值较小，说明参数显著为0,除掉这两项重新进行估计，输入如下程序: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脚0.300010.061654冷7=1jan96d;run;11、提交程序，并把预测值记录下来。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

16、10天的旅客流量。114654911814214815792111110120140936466597377312725445057573030303353626535606357366870666155748560537097957763766845566768857771846471355911080885689657266604251661288569576231838666595180866960584448514954332943110105625355613664615955666656636258605544444050545251444449386932518569896567

17、5651474052777768616475688058585650626260626159595341383650403742475331484651464648776564819277554552618010383817655495460607856615348667458516572767486644051626358645168708285745751305565828977706768789687100111939390103116878264856637557456333851547690747273765685403836467287117100806678667394104931009797655861735238274068825796117653453436366731061147370107110123179107794637575562514753635768667667986810014112011385836136626956587310115714911415462505152466347314955716766786866797737517172687614516815814322522819013319215214