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Type 'q()' to quit R. > x <- c(2487,3644,2501,1629,987,1100,690,1378,1376,1736,2800,2671,2508,3590,2691,1629,1020,1224,787,1424,1232,2021,2782,2682,3284,3194,2736,1701,1089,1240,799,1163,1180,1960,2914,2658,3254,3222,2987,1604,1032,1283,774,1109,1453,1849,2800,3310,3060,3422,3448,1670,1022,1391,767,1172,1498,1623,2646,3439) > par10 = 'FALSE' > par9 = '0' > par8 = '2' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '0' > par2 = '0.2' > 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 ar2 ar3 sar1 sar2 -0.2043 -0.0150 0.3783 -0.0354 -0.3853 s.e. 0.1743 0.1801 0.1802 0.1896 0.3594 sigma^2 estimated as 0.00545: log likelihood = 40.54, aic = -69.07 > (forecast <- predict(arima.out,fx)) $pred Time Series: Start = 49 End = 60 Frequency = 1 [1] 4.894456 5.066766 5.030951 4.328729 3.989611 4.211655 3.759144 4.135328 [9] 4.309127 4.504651 4.878702 5.057348 $se Time Series: Start = 49 End = 60 Frequency = 1 [1] 0.07382510 0.07535010 0.07537597 0.08032288 0.08112867 0.08117230 [7] 0.08180160 0.08205346 0.08208208 0.08215603 0.08221736 0.08222955 > (lb <- forecast$pred - 1.96 * forecast$se) Time Series: Start = 49 End = 60 Frequency = 1 [1] 4.749759 4.919080 4.883214 4.171296 3.830599 4.052557 3.598813 3.974503 [9] 4.148246 4.343626 4.717556 4.896178 > (ub <- forecast$pred + 1.96 * forecast$se) Time Series: Start = 49 End = 60 Frequency = 1 [1] 5.039153 5.214452 5.178688 4.486161 4.148623 4.370752 3.919475 4.296153 [9] 4.470008 4.665677 5.039848 5.218518 > if (par2 == 0) { + x <- exp(x) + forecast$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] 2487.000 3644.000 2501.000 1629.000 987.000 1100.000 690.000 1378.000 [9] 1376.000 1736.000 2800.000 2671.000 2508.000 3590.000 2691.000 1629.000 [17] 1020.000 1224.000 787.000 1424.000 1232.000 2021.000 2782.000 2682.000 [25] 3284.000 3194.000 2736.000 1701.000 1089.000 1240.000 799.000 1163.000 [33] 1180.000 1960.000 2914.000 2658.000 3254.000 3222.000 2987.000 1604.000 [41] 1032.000 1283.000 774.000 1109.000 1453.000 1849.000 2800.000 3310.000 [49] 2808.809 3339.290 3222.927 1519.854 1010.771 1325.146 750.663 1209.344 [57] 1485.753 1854.838 2763.894 3308.371 > (perc.se <- (ub-forecast$pred)/1.96/forecast$pred) Time Series: Start = 49 End = 60 Frequency = 1 [1] 0.08001003 0.07882013 0.07944304 0.09977737 0.11010926 0.10392713 [7] 0.11848899 0.10723300 0.10262434 0.09794694 0.09001483 0.08664651 > postscript(file="/var/www/html/freestat/rcomp/tmp/1ehhp1229267325.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$pred[i]) / locSD) + } > perf.mape = perf.mape / fx > perf.mse = perf.mse / fx > perf.rmse = sqrt(perf.mse) > postscript(file="/var/www/html/freestat/rcomp/tmp/25rsy1229267326.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub))) > dum <- forecast$pred > dum[1:12] <- x[(nx+1):lx] > lines(dum, lty=1) > lines(ub,lty=3) > lines(lb,lty=3) > dev.off() null device 1 > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'time',1,header=TRUE) > a<-table.element(a,'Y[t]',1,header=TRUE) > a<-table.element(a,'F[t]',1,header=TRUE) > a<-table.element(a,'95% LB',1,header=TRUE) > a<-table.element(a,'95% UB',1,header=TRUE) > a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE) > a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE) > a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE) > mylab <- paste('P(F[t]>Y[',nx,sep='') > mylab <- paste(mylab,'])',sep='') > a<-table.element(a,mylab,1,header=TRUE) > a<-table.row.end(a) > for (i in (nx-par5):nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.element(a,'-') + a<-table.row.end(a) + } > for (i in 1:fx) { + a<-table.row.start(a) + a<-table.element(a,nx+i,header=TRUE) + a<-table.element(a,round(x[nx+i],4)) + a<-table.element(a,round(forecast$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/freestat/rcomp/tmp/3izh51229267326.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/freestat/rcomp/tmp/4vyf81229267326.tab") > > system("convert tmp/1ehhp1229267325.ps tmp/1ehhp1229267325.png") > system("convert tmp/25rsy1229267326.ps tmp/25rsy1229267326.png") > > > proc.time() user system elapsed 1.620 0.542 1.849