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Type 'q()' to quit R. > x <- c(117.09,116.77,119.39,122.49,124.08,118.29,112.94,113.79,114.43,118.70,120.36,118.27,118.34,117.82,117.65,118.18,121.02,124.78,131.16,130.14,131.75,134.73,135.35,140.32,136.35,131.60,128.90,133.89,138.25,146.23,144.76,149.30,156.80,159.08,165.12,163.14,153.43,151.01,154.72,154.58,155.63,161.67,163.51,162.91,164.80,164.98,154.54,148.60,149.19,150.61) > par10 = 'FALSE' > par9 = '1' > par8 = '0' > par7 = '1' > par6 = '0' > par5 = '12' > par4 = '0' > par3 = '0' > par2 = '1' > par1 = '12' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > par1 <- as.numeric(par1) #cut off periods > par2 <- as.numeric(par2) #lambda > par3 <- as.numeric(par3) #degree of non-seasonal differencing > par4 <- as.numeric(par4) #degree of seasonal differencing > par5 <- as.numeric(par5) #seasonal period > par6 <- as.numeric(par6) #p > par7 <- as.numeric(par7) #q > par8 <- as.numeric(par8) #P > par9 <- as.numeric(par9) #Q > if (par10 == 'TRUE') par10 <- TRUE > if (par10 == 'FALSE') par10 <- FALSE > if (par2 == 0) x <- log(x) > if (par2 != 0) x <- x^par2 > lx <- length(x) > first <- lx - 2*par1 > nx <- lx - par1 > nx1 <- nx + 1 > fx <- lx - nx > if (fx < 1) { + fx <- par5 + nx1 <- lx + fx - 1 + first <- lx - 2*fx + } > first <- 1 > if (fx < 3) fx <- round(lx/10,0) > (arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML')) Call: arima(x = x[1:nx], order = c(par6, par3, par7), seasonal = list(order = c(par8, par4, par9), period = par5), include.mean = par10, method = "ML") Coefficients: ma1 sma1 0.9999 0.9993 s.e. 0.0548 0.2550 sigma^2 estimated as 1510: log likelihood = -202.91, aic = 411.82 > (forecast <- predict(arima.out,fx)) $pred Time Series: Start = 39 End = 50 Frequency = 1 [1] 112.47133 71.94996 74.19746 76.85496 71.22497 75.35246 80.33246 [8] 81.61996 86.25496 76.27020 64.85198 65.62198 $se Time Series: Start = 39 End = 50 Frequency = 1 [1] 43.77903 61.42729 61.42729 61.42729 61.42729 61.42729 61.42729 61.42729 [9] 61.42729 60.81977 60.18621 60.18621 > (lb <- forecast$pred - 1.96 * forecast$se) Time Series: Start = 39 End = 50 Frequency = 1 [1] 26.66444 -48.44753 -46.20003 -43.54253 -49.17252 -45.04503 -40.06503 [8] -38.77753 -34.14253 -42.93654 -53.11299 -52.34300 > (ub <- forecast$pred + 1.96 * forecast$se) Time Series: Start = 39 End = 50 Frequency = 1 [1] 198.2782 192.3474 194.5949 197.2524 191.6225 195.7500 200.7299 202.0174 [9] 206.6524 195.4769 182.8170 183.5870 > if (par2 == 0) { + x <- exp(x) + forecast$pred <- exp(forecast$pred) + lb <- exp(lb) + ub <- exp(ub) + } > if (par2 != 0) { + x <- x^(1/par2) + forecast$pred <- forecast$pred^(1/par2) + lb <- lb^(1/par2) + ub <- ub^(1/par2) + } > if (par2 < 0) { + olb <- lb + lb <- ub + ub <- olb + } > (actandfor <- c(x[1:nx], forecast$pred)) [1] 117.09000 116.77000 119.39000 122.49000 124.08000 118.29000 112.94000 [8] 113.79000 114.43000 118.70000 120.36000 118.27000 118.34000 117.82000 [15] 117.65000 118.18000 121.02000 124.78000 131.16000 130.14000 131.75000 [22] 134.73000 135.35000 140.32000 136.35000 131.60000 128.90000 133.89000 [29] 138.25000 146.23000 144.76000 149.30000 156.80000 159.08000 165.12000 [36] 163.14000 153.43000 151.01000 112.47133 71.94996 74.19746 76.85496 [43] 71.22497 75.35246 80.33246 81.61996 86.25496 76.27020 64.85198 [50] 65.62198 > (perc.se <- (ub-forecast$pred)/1.96/forecast$pred) Time Series: Start = 39 End = 50 Frequency = 1 [1] 0.3892461 0.8537501 0.8278894 0.7992625 0.8624404 0.8151995 0.7646634 [8] 0.7526013 0.7121595 0.7974250 0.9280551 0.9171655 > postscript(file="/var/www/html/rcomp/tmp/1d8m01229424866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mar=c(4,4,2,2),las=1) > ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub)) > plot(x,ylim=ylim,type='n',xlim=c(first,lx)) > usr <- par('usr') > rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon') > rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender') > abline(h= (-3:3)*2 , col ='gray', lty =3) > polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA) > lines(nx1:lx, lb , lty=2) > lines(nx1:lx, ub , lty=2) > lines(x, lwd=2) > lines(nx1:lx, forecast$pred , lwd=2 , col ='white') > box() > par(opar) > dev.off() null device 1 > prob.dec <- array(NA, dim=fx) > prob.sdec <- array(NA, dim=fx) > prob.ldec <- array(NA, dim=fx) > prob.pval <- array(NA, dim=fx) > perf.pe <- array(0, dim=fx) > perf.mape <- array(0, dim=fx) > perf.se <- array(0, dim=fx) > perf.mse <- array(0, dim=fx) > perf.rmse <- array(0, dim=fx) > for (i in 1:fx) { + locSD <- (ub[i] - forecast$pred[i]) / 1.96 + perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i] + perf.mape[i] = perf.mape[i] + abs(perf.pe[i]) + perf.se[i] = (x[nx+i] - forecast$pred[i])^2 + perf.mse[i] = perf.mse[i] + perf.se[i] + prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD) + prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD) + prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD) + prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD) + } > perf.mape = perf.mape / fx > perf.mse = perf.mse / fx > perf.rmse = sqrt(perf.mse) > postscript(file="/var/www/html/rcomp/tmp/21jlz1229424866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub))) > dum <- forecast$pred > dum[1:12] <- x[(nx+1):lx] > lines(dum, lty=1) > lines(ub,lty=3) > lines(lb,lty=3) > dev.off() null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'time',1,header=TRUE) > a<-table.element(a,'Y[t]',1,header=TRUE) > a<-table.element(a,'F[t]',1,header=TRUE) > a<-table.element(a,'95% LB',1,header=TRUE) > a<-table.element(a,'95% UB',1,header=TRUE) > a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE) > a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE) > a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE) > mylab <- paste('P(F[t]>Y[',nx,sep='') > mylab <- paste(mylab,'])',sep='') > a<-table.element(a,mylab,1,header=TRUE) > a<-table.row.end(a) > for (i in (nx-par5):nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.row.end(a) + } > for (i in 1:fx) { + a<-table.row.start(a) + a<-table.element(a,nx+i,header=TRUE) + a<-table.element(a,round(x[nx+i],4)) + a<-table.element(a,round(forecast$pred[i],4)) + a<-table.element(a,round(lb[i],4)) + a<-table.element(a,round(ub[i],4)) + a<-table.element(a,round((1-prob.pval[i]),4)) + a<-table.element(a,round((1-prob.dec[i]),4)) + a<-table.element(a,round((1-prob.sdec[i]),4)) + a<-table.element(a,round((1-prob.ldec[i]),4)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/3uru21229424866.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'time',1,header=TRUE) > a<-table.element(a,'% S.E.',1,header=TRUE) > a<-table.element(a,'PE',1,header=TRUE) > a<-table.element(a,'MAPE',1,header=TRUE) > a<-table.element(a,'Sq.E',1,header=TRUE) > a<-table.element(a,'MSE',1,header=TRUE) > a<-table.element(a,'RMSE',1,header=TRUE) > a<-table.row.end(a) > for (i in 1:fx) { + a<-table.row.start(a) + a<-table.element(a,nx+i,header=TRUE) + a<-table.element(a,round(perc.se[i],4)) + a<-table.element(a,round(perf.pe[i],4)) + a<-table.element(a,round(perf.mape[i],4)) + a<-table.element(a,round(perf.se[i],4)) + a<-table.element(a,round(perf.mse[i],4)) + a<-table.element(a,round(perf.rmse[i],4)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/49tjq1229424866.tab") > > system("convert tmp/1d8m01229424866.ps tmp/1d8m01229424866.png") > system("convert tmp/21jlz1229424866.ps tmp/21jlz1229424866.png") > > > proc.time() user system elapsed 0.600 0.314 0.780