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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 computationTue, 06 Dec 2011 19:37:21 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/06/t1323218273kjmv7dd11z3npwx.htm/, Retrieved Mon, 29 Apr 2024 01:28:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152057, Retrieved Mon, 29 Apr 2024 01:28:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
-    D      [ARIMA Forecasting] [WS 9 Forecasting ...] [2010-12-03 22:01:04] [8081b8996d5947580de3eb171e82db4f]
-   PD        [ARIMA Forecasting] [Workshop 9, Forecast] [2010-12-05 20:21:31] [3635fb7041b1998c5a1332cf9de22bce]
-   P           [ARIMA Forecasting] [ARIMA Extrapolati...] [2010-12-06 22:58:10] [3635fb7041b1998c5a1332cf9de22bce]
-   P             [ARIMA Forecasting] [Verbetering WS9] [2010-12-14 19:20:19] [3635fb7041b1998c5a1332cf9de22bce]
- R PD                [ARIMA Forecasting] [Ws9 ARIMA voorspe...] [2011-12-07 00:37:21] [635499bc27d9f41bf7bccae25a54e146] [Current]
- R                     [ARIMA Forecasting] [Ws9 ARIMA voorspe...] [2011-12-07 00:41:11] [43a0606d8103c0ba382f0586f4417c48]
- R P                   [ARIMA Forecasting] [Paper arima forecast] [2011-12-19 22:18:45] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152057&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152057&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152057&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 time2 seconds
R Server'George Udny Yule' @ yule.wessa.net






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[60])
5962-------
6058-------
613963.187937.006685.50320.01680.67570.67570.6757
624963.142134.420287.2390.1250.97520.97520.6621
635860.827530.720985.690.41180.82440.82440.5882
644761.678227.750989.08240.14690.60380.60380.6038
654262.425625.234291.90740.08720.84740.84740.6157
666261.775322.386292.45380.49430.89680.89680.5953
673961.668219.674393.76740.08320.49190.49190.5886
684062.001817.16495.63390.09990.910.910.5922
697261.931414.45696.83030.28590.8910.8910.5874
707061.805211.57497.90780.32820.290.290.5818
715461.88228.640799.21420.33950.3350.3350.5808
726561.9125.3294100.40360.43750.65650.65650.5789

\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[60]) \tabularnewline
59 & 62 & - & - & - & - & - & - & - \tabularnewline
60 & 58 & - & - & - & - & - & - & - \tabularnewline
61 & 39 & 63.1879 & 37.0066 & 85.5032 & 0.0168 & 0.6757 & 0.6757 & 0.6757 \tabularnewline
62 & 49 & 63.1421 & 34.4202 & 87.239 & 0.125 & 0.9752 & 0.9752 & 0.6621 \tabularnewline
63 & 58 & 60.8275 & 30.7209 & 85.69 & 0.4118 & 0.8244 & 0.8244 & 0.5882 \tabularnewline
64 & 47 & 61.6782 & 27.7509 & 89.0824 & 0.1469 & 0.6038 & 0.6038 & 0.6038 \tabularnewline
65 & 42 & 62.4256 & 25.2342 & 91.9074 & 0.0872 & 0.8474 & 0.8474 & 0.6157 \tabularnewline
66 & 62 & 61.7753 & 22.3862 & 92.4538 & 0.4943 & 0.8968 & 0.8968 & 0.5953 \tabularnewline
67 & 39 & 61.6682 & 19.6743 & 93.7674 & 0.0832 & 0.4919 & 0.4919 & 0.5886 \tabularnewline
68 & 40 & 62.0018 & 17.164 & 95.6339 & 0.0999 & 0.91 & 0.91 & 0.5922 \tabularnewline
69 & 72 & 61.9314 & 14.456 & 96.8303 & 0.2859 & 0.891 & 0.891 & 0.5874 \tabularnewline
70 & 70 & 61.8052 & 11.574 & 97.9078 & 0.3282 & 0.29 & 0.29 & 0.5818 \tabularnewline
71 & 54 & 61.8822 & 8.6407 & 99.2142 & 0.3395 & 0.335 & 0.335 & 0.5808 \tabularnewline
72 & 65 & 61.912 & 5.3294 & 100.4036 & 0.4375 & 0.6565 & 0.6565 & 0.5789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152057&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[60])[/C][/ROW]
[ROW][C]59[/C][C]62[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]60[/C][C]58[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]39[/C][C]63.1879[/C][C]37.0066[/C][C]85.5032[/C][C]0.0168[/C][C]0.6757[/C][C]0.6757[/C][C]0.6757[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]63.1421[/C][C]34.4202[/C][C]87.239[/C][C]0.125[/C][C]0.9752[/C][C]0.9752[/C][C]0.6621[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]60.8275[/C][C]30.7209[/C][C]85.69[/C][C]0.4118[/C][C]0.8244[/C][C]0.8244[/C][C]0.5882[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]61.6782[/C][C]27.7509[/C][C]89.0824[/C][C]0.1469[/C][C]0.6038[/C][C]0.6038[/C][C]0.6038[/C][/ROW]
[ROW][C]65[/C][C]42[/C][C]62.4256[/C][C]25.2342[/C][C]91.9074[/C][C]0.0872[/C][C]0.8474[/C][C]0.8474[/C][C]0.6157[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]61.7753[/C][C]22.3862[/C][C]92.4538[/C][C]0.4943[/C][C]0.8968[/C][C]0.8968[/C][C]0.5953[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]61.6682[/C][C]19.6743[/C][C]93.7674[/C][C]0.0832[/C][C]0.4919[/C][C]0.4919[/C][C]0.5886[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]62.0018[/C][C]17.164[/C][C]95.6339[/C][C]0.0999[/C][C]0.91[/C][C]0.91[/C][C]0.5922[/C][/ROW]
[ROW][C]69[/C][C]72[/C][C]61.9314[/C][C]14.456[/C][C]96.8303[/C][C]0.2859[/C][C]0.891[/C][C]0.891[/C][C]0.5874[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]61.8052[/C][C]11.574[/C][C]97.9078[/C][C]0.3282[/C][C]0.29[/C][C]0.29[/C][C]0.5818[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]61.8822[/C][C]8.6407[/C][C]99.2142[/C][C]0.3395[/C][C]0.335[/C][C]0.335[/C][C]0.5808[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]61.912[/C][C]5.3294[/C][C]100.4036[/C][C]0.4375[/C][C]0.6565[/C][C]0.6565[/C][C]0.5789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152057&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152057&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[60])
5962-------
6058-------
613963.187937.006685.50320.01680.67570.67570.6757
624963.142134.420287.2390.1250.97520.97520.6621
635860.827530.720985.690.41180.82440.82440.5882
644761.678227.750989.08240.14690.60380.60380.6038
654262.425625.234291.90740.08720.84740.84740.6157
666261.775322.386292.45380.49430.89680.89680.5953
673961.668219.674393.76740.08320.49190.49190.5886
684062.001817.16495.63390.09990.910.910.5922
697261.931414.45696.83030.28590.8910.8910.5874
707061.805211.57497.90780.32820.290.290.5818
715461.88228.640799.21420.33950.3350.3350.5808
726561.9125.3294100.40360.43750.65650.65650.5789






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
610.1802-0.38280585.054400
620.1947-0.2240.3034199.9994392.526919.8123
630.2085-0.04650.21787.995264.349616.2588
640.2267-0.2380.2228215.4496252.124615.8784
650.241-0.32720.2437417.2057285.140816.8861
660.25340.00360.20370.0505237.625815.4151
670.2656-0.36760.2271513.8465277.085916.6459
680.2768-0.35490.2431484.0808302.960217.4058
690.28750.16260.2341101.3768280.562116.75
700.2980.13260.22467.154259.221316.1004
710.3078-0.12740.215262.1297241.303915.534
720.31720.04990.20149.5359221.989914.8993

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
61 & 0.1802 & -0.3828 & 0 & 585.0544 & 0 & 0 \tabularnewline
62 & 0.1947 & -0.224 & 0.3034 & 199.9994 & 392.5269 & 19.8123 \tabularnewline
63 & 0.2085 & -0.0465 & 0.2178 & 7.995 & 264.3496 & 16.2588 \tabularnewline
64 & 0.2267 & -0.238 & 0.2228 & 215.4496 & 252.1246 & 15.8784 \tabularnewline
65 & 0.241 & -0.3272 & 0.2437 & 417.2057 & 285.1408 & 16.8861 \tabularnewline
66 & 0.2534 & 0.0036 & 0.2037 & 0.0505 & 237.6258 & 15.4151 \tabularnewline
67 & 0.2656 & -0.3676 & 0.2271 & 513.8465 & 277.0859 & 16.6459 \tabularnewline
68 & 0.2768 & -0.3549 & 0.2431 & 484.0808 & 302.9602 & 17.4058 \tabularnewline
69 & 0.2875 & 0.1626 & 0.2341 & 101.3768 & 280.5621 & 16.75 \tabularnewline
70 & 0.298 & 0.1326 & 0.224 & 67.154 & 259.2213 & 16.1004 \tabularnewline
71 & 0.3078 & -0.1274 & 0.2152 & 62.1297 & 241.3039 & 15.534 \tabularnewline
72 & 0.3172 & 0.0499 & 0.2014 & 9.5359 & 221.9899 & 14.8993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152057&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]61[/C][C]0.1802[/C][C]-0.3828[/C][C]0[/C][C]585.0544[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]62[/C][C]0.1947[/C][C]-0.224[/C][C]0.3034[/C][C]199.9994[/C][C]392.5269[/C][C]19.8123[/C][/ROW]
[ROW][C]63[/C][C]0.2085[/C][C]-0.0465[/C][C]0.2178[/C][C]7.995[/C][C]264.3496[/C][C]16.2588[/C][/ROW]
[ROW][C]64[/C][C]0.2267[/C][C]-0.238[/C][C]0.2228[/C][C]215.4496[/C][C]252.1246[/C][C]15.8784[/C][/ROW]
[ROW][C]65[/C][C]0.241[/C][C]-0.3272[/C][C]0.2437[/C][C]417.2057[/C][C]285.1408[/C][C]16.8861[/C][/ROW]
[ROW][C]66[/C][C]0.2534[/C][C]0.0036[/C][C]0.2037[/C][C]0.0505[/C][C]237.6258[/C][C]15.4151[/C][/ROW]
[ROW][C]67[/C][C]0.2656[/C][C]-0.3676[/C][C]0.2271[/C][C]513.8465[/C][C]277.0859[/C][C]16.6459[/C][/ROW]
[ROW][C]68[/C][C]0.2768[/C][C]-0.3549[/C][C]0.2431[/C][C]484.0808[/C][C]302.9602[/C][C]17.4058[/C][/ROW]
[ROW][C]69[/C][C]0.2875[/C][C]0.1626[/C][C]0.2341[/C][C]101.3768[/C][C]280.5621[/C][C]16.75[/C][/ROW]
[ROW][C]70[/C][C]0.298[/C][C]0.1326[/C][C]0.224[/C][C]67.154[/C][C]259.2213[/C][C]16.1004[/C][/ROW]
[ROW][C]71[/C][C]0.3078[/C][C]-0.1274[/C][C]0.2152[/C][C]62.1297[/C][C]241.3039[/C][C]15.534[/C][/ROW]
[ROW][C]72[/C][C]0.3172[/C][C]0.0499[/C][C]0.2014[/C][C]9.5359[/C][C]221.9899[/C][C]14.8993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152057&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152057&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
610.1802-0.38280585.054400
620.1947-0.2240.3034199.9994392.526919.8123
630.2085-0.04650.21787.995264.349616.2588
640.2267-0.2380.2228215.4496252.124615.8784
650.241-0.32720.2437417.2057285.140816.8861
660.25340.00360.20370.0505237.625815.4151
670.2656-0.36760.2271513.8465277.085916.6459
680.2768-0.35490.2431484.0808302.960217.4058
690.28750.16260.2341101.3768280.562116.75
700.2980.13260.22467.154259.221316.1004
710.3078-0.12740.215262.1297241.303915.534
720.31720.04990.20149.5359221.989914.8993


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1.0 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = 12 ; par2 = 1.4 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 3 ; par7 = 0 ; 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')