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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationMon, 21 Dec 2009 10:12:24 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/21/t1261415673dez55ptjzusig2i.htm/, Retrieved Fri, 03 May 2024 14:45:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70347, Retrieved Fri, 03 May 2024 14:45:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2009-11-19 08:06:13] [639dd97b6eeebe46a3c92d62cb04fb95]
- RMPD      [ARIMA Forecasting] [] [2009-12-14 08:41:55] [639dd97b6eeebe46a3c92d62cb04fb95]
-   PD        [ARIMA Forecasting] [] [2009-12-19 10:06:44] [ea26ab7ea3bba830cfeb08d06278d52c]
- R PD            [ARIMA Forecasting] [Arima forecast 6 ...] [2009-12-21 17:12:24] [4c76f32a7a0cc9034048c3cdcdaf547e] [Current]
-   P               [ARIMA Forecasting] [Arima forecast 12...] [2009-12-21 17:19:37] [9dbb467a28ad600d808a4e47d5e0774e]
-                     [ARIMA Forecasting] [paper] [2010-12-28 17:28:42] [654616a560d52fe6eb611aa3bbf6b3c7]
-   P               [ARIMA Forecasting] [Arima forecast 24...] [2009-12-21 17:25:53] [9dbb467a28ad600d808a4e47d5e0774e]
-                     [ARIMA Forecasting] [paper] [2010-12-28 17:32:12] [654616a560d52fe6eb611aa3bbf6b3c7]
-                   [ARIMA Forecasting] [paper] [2010-12-28 17:26:19] [654616a560d52fe6eb611aa3bbf6b3c7]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
36845
35338
35022
34777
26887
23970
22780
17351
21382
24561
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698
31956
29506
34506
27165
26736
23691
18157
17328
18205
20995
17382
9367

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70347&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70347&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70347&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[54])
4227044-------
4317970-------
4418730-------
4519684-------
4619785-------
4718479-------
4810698-------
4931956-------
5029506-------
5134506-------
5227165-------
5326736-------
5423691-------
551815718269.821813967.743823896.94380.48430.02950.54160.0295
561732819042.502114323.882325315.54490.29610.6090.53890.0732
571820520012.419214823.899227016.97550.30650.77370.53660.1517
582099520115.104314683.742327555.47010.40840.69260.53460.1731
591738218787.314313524.238226098.56260.35320.2770.53290.0943
60936710876.49167725.348515312.97520.25240.0020.53140

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast \tabularnewline
time & Y[t] & F[t] & 95% LB & 95% UB & p-value(H0: Y[t] = F[t]) & P(F[t]>Y[t-1]) & P(F[t]>Y[t-s]) & P(F[t]>Y[54]) \tabularnewline
42 & 27044 & - & - & - & - & - & - & - \tabularnewline
43 & 17970 & - & - & - & - & - & - & - \tabularnewline
44 & 18730 & - & - & - & - & - & - & - \tabularnewline
45 & 19684 & - & - & - & - & - & - & - \tabularnewline
46 & 19785 & - & - & - & - & - & - & - \tabularnewline
47 & 18479 & - & - & - & - & - & - & - \tabularnewline
48 & 10698 & - & - & - & - & - & - & - \tabularnewline
49 & 31956 & - & - & - & - & - & - & - \tabularnewline
50 & 29506 & - & - & - & - & - & - & - \tabularnewline
51 & 34506 & - & - & - & - & - & - & - \tabularnewline
52 & 27165 & - & - & - & - & - & - & - \tabularnewline
53 & 26736 & - & - & - & - & - & - & - \tabularnewline
54 & 23691 & - & - & - & - & - & - & - \tabularnewline
55 & 18157 & 18269.8218 & 13967.7438 & 23896.9438 & 0.4843 & 0.0295 & 0.5416 & 0.0295 \tabularnewline
56 & 17328 & 19042.5021 & 14323.8823 & 25315.5449 & 0.2961 & 0.609 & 0.5389 & 0.0732 \tabularnewline
57 & 18205 & 20012.4192 & 14823.8992 & 27016.9755 & 0.3065 & 0.7737 & 0.5366 & 0.1517 \tabularnewline
58 & 20995 & 20115.1043 & 14683.7423 & 27555.4701 & 0.4084 & 0.6926 & 0.5346 & 0.1731 \tabularnewline
59 & 17382 & 18787.3143 & 13524.2382 & 26098.5626 & 0.3532 & 0.277 & 0.5329 & 0.0943 \tabularnewline
60 & 9367 & 10876.4916 & 7725.3485 & 15312.9752 & 0.2524 & 0.002 & 0.5314 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70347&T=1

[TABLE]
[ROW][C]Univariate ARIMA Extrapolation Forecast[/C][/ROW]
[ROW][C]time[/C][C]Y[t][/C][C]F[t][/C][C]95% LB[/C][C]95% UB[/C][C]p-value(H0: Y[t] = F[t])[/C][C]P(F[t]>Y[t-1])[/C][C]P(F[t]>Y[t-s])[/C][C]P(F[t]>Y[54])[/C][/ROW]
[ROW][C]42[/C][C]27044[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]17970[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]18730[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]19684[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]19785[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]18479[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]10698[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]31956[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]29506[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]51[/C][C]34506[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]52[/C][C]27165[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]53[/C][C]26736[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]54[/C][C]23691[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]55[/C][C]18157[/C][C]18269.8218[/C][C]13967.7438[/C][C]23896.9438[/C][C]0.4843[/C][C]0.0295[/C][C]0.5416[/C][C]0.0295[/C][/ROW]
[ROW][C]56[/C][C]17328[/C][C]19042.5021[/C][C]14323.8823[/C][C]25315.5449[/C][C]0.2961[/C][C]0.609[/C][C]0.5389[/C][C]0.0732[/C][/ROW]
[ROW][C]57[/C][C]18205[/C][C]20012.4192[/C][C]14823.8992[/C][C]27016.9755[/C][C]0.3065[/C][C]0.7737[/C][C]0.5366[/C][C]0.1517[/C][/ROW]
[ROW][C]58[/C][C]20995[/C][C]20115.1043[/C][C]14683.7423[/C][C]27555.4701[/C][C]0.4084[/C][C]0.6926[/C][C]0.5346[/C][C]0.1731[/C][/ROW]
[ROW][C]59[/C][C]17382[/C][C]18787.3143[/C][C]13524.2382[/C][C]26098.5626[/C][C]0.3532[/C][C]0.277[/C][C]0.5329[/C][C]0.0943[/C][/ROW]
[ROW][C]60[/C][C]9367[/C][C]10876.4916[/C][C]7725.3485[/C][C]15312.9752[/C][C]0.2524[/C][C]0.002[/C][C]0.5314[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70347&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70347&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[54])
4227044-------
4317970-------
4418730-------
4519684-------
4619785-------
4718479-------
4810698-------
4931956-------
5029506-------
5134506-------
5227165-------
5326736-------
5423691-------
551815718269.821813967.743823896.94380.48430.02950.54160.0295
561732819042.502114323.882325315.54490.29610.6090.53890.0732
571820520012.419214823.899227016.97550.30650.77370.53660.1517
582099520115.104314683.742327555.47010.40840.69260.53460.1731
591738218787.314313524.238226098.56260.35320.2770.53290.0943
60936710876.49167725.348515312.97520.25240.0020.53140






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
550.1571-0.0062012728.759700
560.1681-0.090.04812939517.38851476123.07411214.9581
570.1786-0.09030.06223266764.04152073003.39661439.7928
580.18870.04370.0576774216.42881748306.65461322.2355
590.1986-0.07480.0611974908.15131793626.9541339.2636
600.2081-0.13880.0742278564.82651874449.93271369.1055

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
55 & 0.1571 & -0.0062 & 0 & 12728.7597 & 0 & 0 \tabularnewline
56 & 0.1681 & -0.09 & 0.0481 & 2939517.3885 & 1476123.0741 & 1214.9581 \tabularnewline
57 & 0.1786 & -0.0903 & 0.0622 & 3266764.0415 & 2073003.3966 & 1439.7928 \tabularnewline
58 & 0.1887 & 0.0437 & 0.0576 & 774216.4288 & 1748306.6546 & 1322.2355 \tabularnewline
59 & 0.1986 & -0.0748 & 0.061 & 1974908.1513 & 1793626.954 & 1339.2636 \tabularnewline
60 & 0.2081 & -0.1388 & 0.074 & 2278564.8265 & 1874449.9327 & 1369.1055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70347&T=2

[TABLE]
[ROW][C]Univariate ARIMA Extrapolation Forecast Performance[/C][/ROW]
[ROW][C]time[/C][C]% S.E.[/C][C]PE[/C][C]MAPE[/C][C]Sq.E[/C][C]MSE[/C][C]RMSE[/C][/ROW]
[ROW][C]55[/C][C]0.1571[/C][C]-0.0062[/C][C]0[/C][C]12728.7597[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]56[/C][C]0.1681[/C][C]-0.09[/C][C]0.0481[/C][C]2939517.3885[/C][C]1476123.0741[/C][C]1214.9581[/C][/ROW]
[ROW][C]57[/C][C]0.1786[/C][C]-0.0903[/C][C]0.0622[/C][C]3266764.0415[/C][C]2073003.3966[/C][C]1439.7928[/C][/ROW]
[ROW][C]58[/C][C]0.1887[/C][C]0.0437[/C][C]0.0576[/C][C]774216.4288[/C][C]1748306.6546[/C][C]1322.2355[/C][/ROW]
[ROW][C]59[/C][C]0.1986[/C][C]-0.0748[/C][C]0.061[/C][C]1974908.1513[/C][C]1793626.954[/C][C]1339.2636[/C][/ROW]
[ROW][C]60[/C][C]0.2081[/C][C]-0.1388[/C][C]0.074[/C][C]2278564.8265[/C][C]1874449.9327[/C][C]1369.1055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70347&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70347&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
550.1571-0.0062012728.759700
560.1681-0.090.04812939517.38851476123.07411214.9581
570.1786-0.09030.06223266764.04152073003.39661439.7928
580.18870.04370.0576774216.42881748306.65461322.2355
590.1986-0.07480.0611974908.15131793626.9541339.2636
600.2081-0.13880.0742278564.82651874449.93271369.1055


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = 0.0 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 6 ; par2 = 0.0 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
R code (references can be found in the software module):
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'))
(forecast <- predict(arima.out,par1))
(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 <- lb
lb <- ub
ub <- 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.mape <- array(0, dim=fx)
perf.mape1 <- 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)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
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[1] = abs(perf.pe[1])
perf.mse[1] = abs(perf.se[1])
for (i in 2:fx) {
perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
perf.mape1[i] = perf.mape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
}
perf.rmse = sqrt(perf.mse1)
bitmap(file='test2.png')
plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$pred
dum[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',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.mape1[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.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')