时间序列分析实验报告

《时间序列分析》课程实验报告项目名称:组员姓名:指导教师:完成日期:非平稳序列确定性分析智情瑶牛宪华201年月日一、上机练习(P124)拟合线性趋势12.7914.0212.9218.2721.2218.8125.7326.2726.7528.7331.7133.95dataa;inputgov_cons@@;time=intnx('year','1jan1981'd,_n_-1);formattimeyear2.;t=_n_;cards;12.7914.0212.9218.2721.2218.8125.7326.2726.7528.7331.7133.95;procgplot;plotgov_cons*time=1;symbol1c=blackv=stari=join;run;procautoreg;modelgov_cons=t;outputout=outp=forecast;procgplotdata=out;plotgov_cons*time=1forecast*time=2/overlayhaxis='1jan1981'dto'1jan1993'dbyyear;symbol2c=redv=nonei=joinw=2l=3;run;

TheAUTOREGProcedureDependentVariablegov_consOrdinaryLeastSquaresEstimates26.1902206DFE102.61902RootMSE1.6183448.3900913AIC47.420278R-Square0.9555Tota1R-Square0.9555W：=i+■■nn9VariableDFEstimateStandardErrortValueApproxPr>|t|Intercept19.70860.99609.75<.0001t11.98290.135314.65<.0001分析：AIC的值，故拒绝原由上图可得DW的统计量等于2.7269,R方等于0.9555,SBC的值为48.3900913,为47.420278.一元线性模型的截距等于9.7086，系数等于1.9829，且AIC的值，故拒绝原拟合非线性趋势1.857.4814.2923.0237.4274.27140.72265.81528.231040.272064.254113.738212.2116405.95datab;inputindex@@;time=intnx('year',*1jan1991'd,_n_-1);formattimeyear2.;t=_n_;t2=t**2;cards;1.857.4814.2923.0237.4274.27140.72265.81528.231040.272064.254113.738212.2116405.95;procgplot;plotindex*time=1;symbol1c=blackv=stari=join;procreg;modelindex=tt2;modelindex=t2;outputout=outp=index_cup;procgplotdata=out;

plotindex*time=1index_cup*time=2/overlay;symbol2c=redv=nonei=joinw=2l=3;run;NumberofObservationsReadNumberofObservationsUsedTheREGProcedureModel:MODEL2DependentVariable：indexSourceDFSumofSquaresMeanSquareFValuePr>FMode1119321100319321100326.180.0003Error12885741327381178CorrectedTotalNumberofObservationsReadNumberofObservationsUsedTheREGProcedureModel:MODEL2DependentVariable：indexSourceDFSumofSquaresMeanSquareFValuePr>FMode1119321100319321100326.180.0003Error12885741327381178CorrectedTotaootMSE2716.83228R-Square0.6857DependentMean2352.10714AdjR-Sq0.6595CoeffYar115.50631AnalysisofVarianceParameterEstimatesVariableDFEstimateErrortValuePr>|t|Intercept1-1980.579781115.51529-1.780.1012t2159.7612011.680635.120.0003ParameterStandard分析:P<0.0001，所以模型显著。方差结果显示，R2=0.8435，说明因变量84.35%由模型确定，P<0.0001，所以模型显著。&=179.988012人..,_p0=3597.94418p=-1905.99568X—11过程407774177843160458974194744061443784723743315433964484346835

4283343548425524352643740450074487846234463544726048527502375045152294&=179.9880124077741778431604589741947440614437847237433154339644843468354283343548425524352643740450074487846234463544726048527502375045152294539905547744637471074503947940466674932547055503184888352605515925515254633588025785061978dataxiti3;inputx@@;t=intnx('quarter',*1jan1978*d,_n_-1);formattyyq4.;cards;4077741778431604589741947440614437847237433154339644843468354283343548446374710742552435264503947940437404500746667493254487846234470555031846354472604888352605485275023751592551525045152294546335880253990554775785061978;procgplotdata=xiti3;plotx*t;symbolc=redv=stari=join;run;procx11data=xiti3;quarterlydate=t;varx;outputout=outb1=xd10=seasond11=adjustedd12=trendd13=irr;dataout;setout;estimate=trend*season/100;procgplotdata=out;plotx*t=1estimate*t=2/overlay;plotadjusted*t=1trend*t=1irr*t=1;symbol1c=redi=joinv=star;symbol2c=blacki=nonev=star;run；运行结果：分析：上图为该序列的时序图，可以很明显的看出其具有长期增长趋势，且具有季节波动，故我们用X-11过程进行拟合。分析：上图为季节调整后的序列值时序图。TRENIi78Q178Q3T'dQl79y380Q180Q381Q181Q382Q182Q383Q183Q384Q184Q385Q185Q386Q186Q387Q187Q388Q188Q389Q15ld0005800057000560005500054000530005200051000500004900048000470004B00045000440004300042000争GRAPHlTORK.GSEG.GPLOT17□0®分析：上图为趋势拟合值序列时序图。78Q178Q3T'dQl79Q380Q180Q381Q181Q382Q182Q383Q183Q384Q184Q385Q185Q386Q186Q387Q187Q388Q188Q389Q1GRAPH1¥ORK.GSEG.GPLOT18分析：上图为不规则波动值的时序图。78Q178Q3T'dQl79y380Q180Q381Q181Q382Q178Q178Q3T'dQl79y380Q180Q381Q181Q382Q182Q383Q183Q384Q184Q385Q185Q386Q186Q387Q187Q388Q188Q389Q170000600005000040000GRAPH1TORK.GSEG.GPLOT15Forecost过程程序：dataxiti4;inputx@@;t=1949+_n_-1;cards;4077741778431604589741947440614437847237433154339644843468354283343548446374710742552435264503947940437404500746667493254487846234470555031846354472604888352605485275023751592551525045152294546335880253990554775785061978;procgplotdata=xiti4;plotx*t;symbolc=redv=stari=join;run;procforecastdata=xiti4method=stepartrend=2lead=5out=outoutfulloutest=est;idt;varx;run;procgplotdata=out;plotx*t=_type_/href=2008;symbolli=joinv=starc=black;symbol2i=joinv=nonec=green;symbol3i=joinv=nonec=red;symbol4i=joinv=nonec=red;run;分析：由该序列的时序图可知，其具有长期趋势，且含有季节效应，趋势特征基本为线性趋势，即trend=2.

__1I®-1*':+i+：1J-□LLilLLHJ扫-?zTypeofNwiberofPeriodsObservationintotheForecast741985FORECAST051095.657999751986ACTUAL052294761986FORECAST052151.215001771987ACTUAL054B33781987FORECAST053392.555358791988ACTUAL058802801988FORECAST056871.982373811989ACTUAL053990821989FORECAST053523.5B6814831990ACTUAL055477841990FORECAST054962.01587851991ACTUAL057850861991FORECAST056726.6053188T1992ACTUAL061978881992FORECAST060182.051134891993FORECAST156392.325918901993L95153395.20634&911993U'd5159389.445491921994FORECAST257178.648416931994L95253793.631699941994U'd5260563.665132951995FORECAST358777.43-4695961995L95355275.652752971995U'd5362279.216639981996FORECAST461755.286995991996L95458210.9659171001996U95465299.6080731011997FORECAST556732.4654731021997L955523T2.632369-1031997U'd5561092.298577i分析：由上表可以很明显的看到每一年的与序列值、预测值，还有预测的后面六期预测值的95%置信区间。分析：此表为预测过程中相关参数及拟合效果，可以看到RSQUARE=0.9574111，拟合效果很好。

分析：上图为预测效果图，其中绿色的线段表示预测值，红色的代表预测的5期值的95%置信区间，黑色的为原序列，可以看出其预测效果很好。二、课后习题7.某地区1962-1970年平均每头奶牛的月度产奶量数据（单位：磅）具体数据详见书P123589561640656727697640599568577553582600566653673742716660617583587565598628618688705770736678639604611594634658622709722782756702653615521602635677635736755811798735697661667645688713667762784837817767722681687660698717696775796858826783740701706677711734690785805871845801764725723690734750707807824886859819783740747711751（1）绘制该序列的时序图，直观考察该序列的特点。dataxl；inputx@@;t=intnx('month',*1jan1993*d,_n_-1);formattyear4.;cards;589561640656727697640599568577553582600566653673742716660617583587565598628618688705770736678639604611594634658622709722782756702653615521602635677635736755811798735697661667645688713667762784837817767722681687660698717696775796858826783740701706677711734690785805871845801764725723690734750707807824886859819783740747711751,procgplot;plotx*t;symbolc=redi=joinv=star;run;(2)datax1;setout;x=x_b1;season=x_d10;adjusted=x_d11;trend=x_d12;irr=x_d13;estimate=season*trend;procgplotdata（4）使用X-11方法，确定该序列的趋势。8.某城市1980年1月至1995年8月每月屠宰生猪数量（单位：头）（数据详见书P203）选择适当地模型拟合该序列的发展，并预测1995年9月至1997年9月该城市生猪屠宰数量。datalianxi2;inputx@@;t=intnx（'month','1jan1980'd,_n_-1）;formattdate.;cards;763787194733873964281050849574111064710033194133103055905951014577688981291916439622810273610026410349197027952409168010125910956476892857739521093771982029792210030694089102680779199356111703281225883571061759192210411410995997880105386964799758010949011019190974989811071889417711509711369611453212011093607110925103312120184103069103351111331106161111590994471019878533386970100561895468926582719794987484673819770297844686978758786957175722641827735763292593807833272381559716975085472701337912585805817788685269069795568817466698722587344576131860827544373969781397864666269737768003470694818237564075540822297534577034785897976975982780747758884100979668905193503847477453191900816358979781022782657727185043954187956810328395770912971012441145251011399386695171100183103926102643108387970779090190336887328375999267732927894394399929379013091055106062103560104075101783937911023138241383534109011964991024301