## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationWed, 08 Apr 2020 05:11:20 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Apr/08/t1586315568s46ll08fqnbdgto.htm/, Retrieved Sat, 15 May 2021 23:47:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319118, Retrieved Sat, 15 May 2021 23:47:04 +0000
QR Codes:

Original text written by user:C
IsPrivate?No (this computation is public)
User-defined keywordsB
Estimated Impact43
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Forecasting] [A] [2020-04-08 03:11:20] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message
Dataseries X:
112
118
132
129
121
135
148
148
136
119
104
118
115
126
141
135
125
149
170
170
158
133
114
140
145
150
178
163
172
178
199
199
184
162
146
166
171
180
193
181
183
218
230
242
209
191
172
194
196
196
236
235
229
243
264
272
237
211
180
201
204
188
235
227
234
264
302
293
259
229
203
229
242
233
267
269
270
315
364
347
312
274
237
278
284
277
317
313
318
374
413
405
355
306
271
306
315
301
356
348
355
422
465
467
404
347
305
336
340
318
362
348
363
435
491
505
404
359
310
337
360
342
406
396
420
472
548
559
463
407
362
405
417
391
419
461
472
535
622
606
508
461
390
432

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 0 seconds R Server Big Analytics Cloud Computing Center R Engine error message Error in xy.coords(x, y, setLab = FALSE) : 'x' and 'y' lengths differ Calls: polygon -> xy.coords Execution halted 

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & Error in xy.coords(x, y, setLab = FALSE) : 'x' and 'y' lengths differ
Calls: polygon -> xy.coords
Execution halted
\tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319118&T=0

[TABLE]
[ROW]
 Summary of computational transaction[/C][/ROW] [ROW] Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW] Raw Output[/C] view raw output of R engine [/C][/ROW] [ROW] Computing time[/C] 0 seconds[/C][/ROW] [ROW] R Server[/C] Big Analytics Cloud Computing Center[/C][/ROW] [ROW] R Engine error message[/C][C]Error in xy.coords(x, y, setLab = FALSE) : 'x' and 'y' lengths differ Calls: polygon -> xy.coords Execution halted [/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319118&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319118&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 0 seconds R Server Big Analytics Cloud Computing Center R Engine error message Error in xy.coords(x, y, setLab = FALSE) : 'x' and 'y' lengths differ Calls: polygon -> xy.coords Execution halted 

Parameters (Session):
par1 = 0 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 0 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #cut off periodspar2 <- as.numeric(par2) #lambdapar3 <- as.numeric(par3) #degree of non-seasonal differencingpar4 <- as.numeric(par4) #degree of seasonal differencingpar5 <- as.numeric(par5) #seasonal periodpar6 <- as.numeric(par6) #ppar7 <- as.numeric(par7) #qpar8 <- as.numeric(par8) #Ppar9 <- as.numeric(par9) #Qif (par10 == 'TRUE') par10 <- TRUEif (par10 == 'FALSE') par10 <- FALSEif (par2 == 0) x <- log(x)if (par2 != 0) x <- x^par2lx <- length(x)first <- lx - 2*par1nx <- lx - par1nx1 <- nx + 1fx <- lx - nxif (fx < 1) {fx <- par5*2nx1 <- lx + fx - 1first <- lx - 2*fx}first <- 1if (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'))(forecast <- predict(arima.out,fx))(lb <- forecast$pred - 1.96 * forecast$se)(ub <- forecast$pred + 1.96 * forecast$se)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 <- lblb <- ubub <- olb}(actandfor <- c(x[1:nx], forecast$pred))(perc.se <- (ub-forecast$pred)/1.96/forecast$pred)bitmap(file='test1.png')opar <- par(mar=c(4,4,2,2),las=1)ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))plot(x,ylim=ylim,type='n',xlim=c(first,lx))usr <- par('usr')rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')abline(h= (-3:3)*2 , col ='gray', lty =3)polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)lines(nx1:lx, lb , lty=2)lines(nx1:lx, ub , lty=2)lines(x, lwd=2)lines(nx1:lx, forecast$pred , lwd=2 , col ='white')box()par(opar)dev.off()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.spe <- array(0, dim=fx)perf.scalederr <- array(0, dim=fx)perf.mase <- array(0, dim=fx)perf.mase1 <- array(0, dim=fx)perf.mape <- array(0, dim=fx)perf.smape <- array(0, dim=fx)perf.mape1 <- array(0, dim=fx)perf.smape1 <- array(0,dim=fx)perf.se <- array(0, dim=fx)perf.mse <- array(0, dim=fx)perf.mse1 <- array(0, dim=fx)perf.rmse <- array(0, dim=fx)perf.scaleddenom <- 0for (i in 2:fx) {perf.scaleddenom = perf.scaleddenom + abs(x[nx+i] - x[nx+i-1])}perf.scaleddenom = perf.scaleddenom / (fx-1)for (i in 1:fx) {locSD <- (ub[i] - forecast$pred[i]) / 1.96perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenomperf.pe[i] = (x[nx+i] - forecast$pred[i]) / x[nx+i]perf.spe[i] = 2*(x[nx+i] - forecast$pred[i]) / (x[nx+i] + forecast$pred[i])perf.se[i] = (x[nx+i] - forecast$pred[i])^2prob.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[1] = abs(perf.pe[1])perf.smape[1] = abs(perf.spe[1])perf.mape1[1] = perf.mape[1]perf.smape1[1] = perf.smape[1]perf.mse[1] = perf.se[1]perf.mase[1] = abs(perf.scalederr[1])perf.mase1[1] = perf.mase[1]for (i in 2:fx) {perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])perf.mape1[i] = perf.mape[i] / iperf.smape[i] = perf.smape[i-1] + abs(perf.spe[i])perf.smape1[i] = perf.smape[i] / iperf.mse[i] = perf.mse[i-1] + perf.se[i]perf.mse1[i] = perf.mse[i] / iperf.mase[i] = perf.mase[i-1] + abs(perf.scalederr[i])perf.mase1[i] = perf.mase[i] / i}perf.rmse = sqrt(perf.mse1)bitmap(file='test2.png')plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))dum <- forecast$preddum[1:par1] <- x[(nx+1):lx]lines(dum, lty=1)lines(ub,lty=3)lines(lb,lty=3)dev.off()load(file='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='mytable.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',10,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,'sMAPE',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.element(a,'ScaledE',1,header=TRUE)a<-table.element(a,'MASE',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.mape1[i],4))a<-table.element(a,round(perf.smape1[i],4))a<-table.element(a,round(perf.se[i],4))a<-table.element(a,round(perf.mse1[i],4))a<-table.element(a,round(perf.rmse[i],4))a<-table.element(a,round(perf.scalederr[i],4))a<-table.element(a,round(perf.mase1[i],4))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable1.tab')