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Type 'q()' to quit R. > x <- c(96,86,82,92,99,101,102,100,101,100,99,97,97,97,96,92,91,87,82,89,91,90,87,89,95,85,94,94,97,99,97,96,94,100,96,98,98,94,93,94,94,97,98,95,89,89,89,90,86,92,91,95,99,98,95,96,94,98,98,98,98,102,101,92,99,101,99,102,102,101,99,98,98) > par10 = 'FALSE' > par9 = '0' > par8 = '0' > par7 = '1' > par6 = '1' > par5 = '12' > par4 = '0' > par3 = '1' > 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: ar1 ma1 0.6922 -1.0000 s.e. 0.0982 0.0462 sigma^2 estimated as 12.51: log likelihood = -162.16, aic = 330.32 > (forecast <- predict(arima.out,fx)) $pred Time Series: Start = 62 End = 73 Frequency = 1 [1] 96.88872 96.11946 95.58695 95.21833 94.96317 94.78653 94.66426 94.57962 [9] 94.52103 94.48047 94.45239 94.43296 $se Time Series: Start = 62 End = 73 Frequency = 1 [1] 3.563300 4.364138 4.719198 4.893151 4.983157 5.031750 5.059024 5.074908 [9] 5.084482 5.090433 5.094228 5.096702 > (lb <- forecast$pred - 1.96 * forecast$se) Time Series: Start = 62 End = 73 Frequency = 1 [1] 89.90465 87.56575 86.33732 85.62776 85.19618 84.92430 84.74857 84.63280 [9] 84.55544 84.50322 84.46770 84.44342 > (ub <- forecast$pred + 1.96 * forecast$se) Time Series: Start = 62 End = 73 Frequency = 1 [1] 103.8728 104.6732 104.8366 104.8089 104.7302 104.6488 104.5799 104.5264 [9] 104.4866 104.4577 104.4371 104.4225 > if (par2 == 0) { + x <- exp(x) + forecast$pred <- exp(forecast$pred) + lb <- exp(lb) + ub <- exp(ub) + } > if (par2 != 0) { + x <- x^(1/par2) + forecast$pred <- forecast$pred^(1/par2) + lb <- lb^(1/par2) + ub <- ub^(1/par2) + } > (actandfor <- c(x[1:nx], forecast$pred)) [1] 96.00000 86.00000 82.00000 92.00000 99.00000 101.00000 102.00000 [8] 100.00000 101.00000 100.00000 99.00000 97.00000 97.00000 97.00000 [15] 96.00000 92.00000 91.00000 87.00000 82.00000 89.00000 91.00000 [22] 90.00000 87.00000 89.00000 95.00000 85.00000 94.00000 94.00000 [29] 97.00000 99.00000 97.00000 96.00000 94.00000 100.00000 96.00000 [36] 98.00000 98.00000 94.00000 93.00000 94.00000 94.00000 97.00000 [43] 98.00000 95.00000 89.00000 89.00000 89.00000 90.00000 86.00000 [50] 92.00000 91.00000 95.00000 99.00000 98.00000 95.00000 96.00000 [57] 94.00000 98.00000 98.00000 98.00000 98.00000 96.88872 96.11946 [64] 95.58695 95.21833 94.96317 94.78653 94.66426 94.57962 94.52103 [71] 94.48047 94.45239 94.43296 > (perc.se <- (ub-forecast$pred)/1.96/forecast$pred) Time Series: Start = 62 End = 73 Frequency = 1 [1] 0.03677725 0.04540327 0.04937073 0.05138875 0.05247463 0.05308508 [7] 0.05344176 0.05365753 0.05379208 0.05387815 0.05393435 0.05397164 > postscript(file="/var/www/html/rcomp/tmp/1ozgp1197317627.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$pred[i]) / locSD) + } > perf.mape = perf.mape / fx > perf.mse = perf.mse / fx > perf.rmse = sqrt(perf.mse) > postscript(file="/var/www/html/rcomp/tmp/2qzgq1197317627.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub))) > dum <- forecast$pred > dum[1:12] <- x[(nx+1):lx] > lines(dum, lty=1) > lines(ub,lty=3) > lines(lb,lty=3) > dev.off() null device 1 > load(file='/var/www/html/rcomp/createtable') > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'time',1,header=TRUE) > a<-table.element(a,'Y[t]',1,header=TRUE) > a<-table.element(a,'F[t]',1,header=TRUE) > a<-table.element(a,'95% LB',1,header=TRUE) > a<-table.element(a,'95% UB',1,header=TRUE) > a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE) > a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE) > a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE) > mylab <- paste('P(F[t]>Y[',nx,sep='') > mylab <- paste(mylab,'])',sep='') > a<-table.element(a,mylab,1,header=TRUE) > a<-table.row.end(a) > for (i in (nx-par5):nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.row.end(a) + } > for (i in 1:fx) { + a<-table.row.start(a) + a<-table.element(a,nx+i,header=TRUE) + a<-table.element(a,round(x[nx+i],4)) + a<-table.element(a,round(forecast$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/3v5g41197317627.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/4i4dp1197317627.tab") > > system("convert tmp/1ozgp1197317627.ps tmp/1ozgp1197317627.png") > system("convert tmp/2qzgq1197317627.ps tmp/2qzgq1197317627.png") > > > proc.time() user system elapsed 0.857 0.322 0.970