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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 computationFri, 30 Nov 2012 11:34:15 -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/2012/Nov/30/t1354293276p6hhwlc01ikfgaj.htm/, Retrieved Fri, 03 May 2024 23:51:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=195135, Retrieved Fri, 03 May 2024 23:51:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60

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   [(Partial) Autocorrelation Function] [Unemployment] [2010-11-29 09:09:37] [b98453cac15ba1066b407e146608df68]
- RMPD    [Spectral Analysis] [Aantal werklozen ...] [2012-11-30 13:59:58] [3e2c7966ca4198d187b4c59e4eb5d004]
- R P       [Spectral Analysis] [Aantal werklozen ...] [2012-11-30 14:10:07] [3e2c7966ca4198d187b4c59e4eb5d004]
-   P         [Spectral Analysis] [Aantal werklozen ...] [2012-11-30 14:14:07] [3e2c7966ca4198d187b4c59e4eb5d004]
- RMP           [Standard Deviation-Mean Plot] [Aantal werklozen ...] [2012-11-30 14:29:02] [3e2c7966ca4198d187b4c59e4eb5d004]
- RMP               [ARIMA Forecasting] [Aantal werklozen ...] [2012-11-30 16:34:15] [7ac586d7aaad1f98cbd1d1bd98b37cf0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
116
111
104
100
93
91
119
139
134
124
113
109
109
106
101
98
93
91
122
139
140
132
117
114
113
110
107
103
98
98
137
148
147
139
130
128
127
123
118
114
108
111
151
159
158
148
138
137
136
133
126
120
114
116
153
162
161
149
139
135
130
127
122
117
112
113
149
157
157
147
137
132
125
123
117
114
111
112
144
150
149
134
123
116
117
111
105
102
95
93
124
130
124
115
106
105
105
101
95
93
84
87
116
120
117
109
105
107
109
109
108
107
99
103
131
137
135
124
118
121
121
118
113
107
100
102
130
136
133
120
112
109
110
106
102
98
92
92
120
127
124
114
108
106
111
110
104
100
96
98
122
134
133

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'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195135&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195135&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195135&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'Gwilym Jenkins' @ jenkins.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[141])
140127-------
141124-------
142114120.3563102.4903143.33620.29390.3780.3780.378
143108117.438893.1386152.65580.29970.57590.57590.3575
144106115.394388.1987157.42120.33060.63490.63490.3441
145111114.044385.505159.67910.4480.63510.63510.3345
146110113.177683.9649160.78080.4480.53570.53570.3279
147104112.62683.0282161.41080.36450.5420.5420.3238
148100112.27282.4142161.86760.31380.62810.62810.3215
14996112.038781.9765162.2690.26570.68070.68070.3203
15098111.877681.6362162.65820.29610.730.730.3199
151122111.759281.3501163.05020.34780.70050.70050.32
152134111.665681.0938163.44980.1990.34780.34780.3203
153133111.586380.8536163.85870.2110.20030.20030.3208

\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[141]) \tabularnewline
140 & 127 & - & - & - & - & - & - & - \tabularnewline
141 & 124 & - & - & - & - & - & - & - \tabularnewline
142 & 114 & 120.3563 & 102.4903 & 143.3362 & 0.2939 & 0.378 & 0.378 & 0.378 \tabularnewline
143 & 108 & 117.4388 & 93.1386 & 152.6558 & 0.2997 & 0.5759 & 0.5759 & 0.3575 \tabularnewline
144 & 106 & 115.3943 & 88.1987 & 157.4212 & 0.3306 & 0.6349 & 0.6349 & 0.3441 \tabularnewline
145 & 111 & 114.0443 & 85.505 & 159.6791 & 0.448 & 0.6351 & 0.6351 & 0.3345 \tabularnewline
146 & 110 & 113.1776 & 83.9649 & 160.7808 & 0.448 & 0.5357 & 0.5357 & 0.3279 \tabularnewline
147 & 104 & 112.626 & 83.0282 & 161.4108 & 0.3645 & 0.542 & 0.542 & 0.3238 \tabularnewline
148 & 100 & 112.272 & 82.4142 & 161.8676 & 0.3138 & 0.6281 & 0.6281 & 0.3215 \tabularnewline
149 & 96 & 112.0387 & 81.9765 & 162.269 & 0.2657 & 0.6807 & 0.6807 & 0.3203 \tabularnewline
150 & 98 & 111.8776 & 81.6362 & 162.6582 & 0.2961 & 0.73 & 0.73 & 0.3199 \tabularnewline
151 & 122 & 111.7592 & 81.3501 & 163.0502 & 0.3478 & 0.7005 & 0.7005 & 0.32 \tabularnewline
152 & 134 & 111.6656 & 81.0938 & 163.4498 & 0.199 & 0.3478 & 0.3478 & 0.3203 \tabularnewline
153 & 133 & 111.5863 & 80.8536 & 163.8587 & 0.211 & 0.2003 & 0.2003 & 0.3208 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195135&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[141])[/C][/ROW]
[ROW][C]140[/C][C]127[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]141[/C][C]124[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]142[/C][C]114[/C][C]120.3563[/C][C]102.4903[/C][C]143.3362[/C][C]0.2939[/C][C]0.378[/C][C]0.378[/C][C]0.378[/C][/ROW]
[ROW][C]143[/C][C]108[/C][C]117.4388[/C][C]93.1386[/C][C]152.6558[/C][C]0.2997[/C][C]0.5759[/C][C]0.5759[/C][C]0.3575[/C][/ROW]
[ROW][C]144[/C][C]106[/C][C]115.3943[/C][C]88.1987[/C][C]157.4212[/C][C]0.3306[/C][C]0.6349[/C][C]0.6349[/C][C]0.3441[/C][/ROW]
[ROW][C]145[/C][C]111[/C][C]114.0443[/C][C]85.505[/C][C]159.6791[/C][C]0.448[/C][C]0.6351[/C][C]0.6351[/C][C]0.3345[/C][/ROW]
[ROW][C]146[/C][C]110[/C][C]113.1776[/C][C]83.9649[/C][C]160.7808[/C][C]0.448[/C][C]0.5357[/C][C]0.5357[/C][C]0.3279[/C][/ROW]
[ROW][C]147[/C][C]104[/C][C]112.626[/C][C]83.0282[/C][C]161.4108[/C][C]0.3645[/C][C]0.542[/C][C]0.542[/C][C]0.3238[/C][/ROW]
[ROW][C]148[/C][C]100[/C][C]112.272[/C][C]82.4142[/C][C]161.8676[/C][C]0.3138[/C][C]0.6281[/C][C]0.6281[/C][C]0.3215[/C][/ROW]
[ROW][C]149[/C][C]96[/C][C]112.0387[/C][C]81.9765[/C][C]162.269[/C][C]0.2657[/C][C]0.6807[/C][C]0.6807[/C][C]0.3203[/C][/ROW]
[ROW][C]150[/C][C]98[/C][C]111.8776[/C][C]81.6362[/C][C]162.6582[/C][C]0.2961[/C][C]0.73[/C][C]0.73[/C][C]0.3199[/C][/ROW]
[ROW][C]151[/C][C]122[/C][C]111.7592[/C][C]81.3501[/C][C]163.0502[/C][C]0.3478[/C][C]0.7005[/C][C]0.7005[/C][C]0.32[/C][/ROW]
[ROW][C]152[/C][C]134[/C][C]111.6656[/C][C]81.0938[/C][C]163.4498[/C][C]0.199[/C][C]0.3478[/C][C]0.3478[/C][C]0.3203[/C][/ROW]
[ROW][C]153[/C][C]133[/C][C]111.5863[/C][C]80.8536[/C][C]163.8587[/C][C]0.211[/C][C]0.2003[/C][C]0.2003[/C][C]0.3208[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195135&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195135&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[141])
140127-------
141124-------
142114120.3563102.4903143.33620.29390.3780.3780.378
143108117.438893.1386152.65580.29970.57590.57590.3575
144106115.394388.1987157.42120.33060.63490.63490.3441
145111114.044385.505159.67910.4480.63510.63510.3345
146110113.177683.9649160.78080.4480.53570.53570.3279
147104112.62683.0282161.41080.36450.5420.5420.3238
148100112.27282.4142161.86760.31380.62810.62810.3215
14996112.038781.9765162.2690.26570.68070.68070.3203
15098111.877681.6362162.65820.29610.730.730.3199
151122111.759281.3501163.05020.34780.70050.70050.32
152134111.665681.0938163.44980.1990.34780.34780.3203
153133111.586380.8536163.85870.2110.20030.20030.3208






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
1420.0974-0.0528040.403100
1430.153-0.08040.066689.091364.74728.0466
1440.1858-0.08140.071588.253172.58258.5195
1450.2042-0.02670.06039.267856.75387.5335
1460.2146-0.02810.053910.097147.42256.8864
1470.221-0.07660.057774.407751.927.2056
1480.2254-0.10930.065150.601766.01748.1251
1490.2287-0.14320.0748257.238789.92019.4826
1500.2316-0.1240.0803192.5878101.327610.0662
1510.23420.09160.0814104.8747101.682310.0838
1520.23660.20.0922498.8266137.786311.7382
1530.2390.19190.1005458.5456164.516312.8264

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
142 & 0.0974 & -0.0528 & 0 & 40.4031 & 0 & 0 \tabularnewline
143 & 0.153 & -0.0804 & 0.0666 & 89.0913 & 64.7472 & 8.0466 \tabularnewline
144 & 0.1858 & -0.0814 & 0.0715 & 88.2531 & 72.5825 & 8.5195 \tabularnewline
145 & 0.2042 & -0.0267 & 0.0603 & 9.2678 & 56.7538 & 7.5335 \tabularnewline
146 & 0.2146 & -0.0281 & 0.0539 & 10.0971 & 47.4225 & 6.8864 \tabularnewline
147 & 0.221 & -0.0766 & 0.0577 & 74.4077 & 51.92 & 7.2056 \tabularnewline
148 & 0.2254 & -0.1093 & 0.065 & 150.6017 & 66.0174 & 8.1251 \tabularnewline
149 & 0.2287 & -0.1432 & 0.0748 & 257.2387 & 89.9201 & 9.4826 \tabularnewline
150 & 0.2316 & -0.124 & 0.0803 & 192.5878 & 101.3276 & 10.0662 \tabularnewline
151 & 0.2342 & 0.0916 & 0.0814 & 104.8747 & 101.6823 & 10.0838 \tabularnewline
152 & 0.2366 & 0.2 & 0.0922 & 498.8266 & 137.7863 & 11.7382 \tabularnewline
153 & 0.239 & 0.1919 & 0.1005 & 458.5456 & 164.5163 & 12.8264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195135&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]142[/C][C]0.0974[/C][C]-0.0528[/C][C]0[/C][C]40.4031[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]143[/C][C]0.153[/C][C]-0.0804[/C][C]0.0666[/C][C]89.0913[/C][C]64.7472[/C][C]8.0466[/C][/ROW]
[ROW][C]144[/C][C]0.1858[/C][C]-0.0814[/C][C]0.0715[/C][C]88.2531[/C][C]72.5825[/C][C]8.5195[/C][/ROW]
[ROW][C]145[/C][C]0.2042[/C][C]-0.0267[/C][C]0.0603[/C][C]9.2678[/C][C]56.7538[/C][C]7.5335[/C][/ROW]
[ROW][C]146[/C][C]0.2146[/C][C]-0.0281[/C][C]0.0539[/C][C]10.0971[/C][C]47.4225[/C][C]6.8864[/C][/ROW]
[ROW][C]147[/C][C]0.221[/C][C]-0.0766[/C][C]0.0577[/C][C]74.4077[/C][C]51.92[/C][C]7.2056[/C][/ROW]
[ROW][C]148[/C][C]0.2254[/C][C]-0.1093[/C][C]0.065[/C][C]150.6017[/C][C]66.0174[/C][C]8.1251[/C][/ROW]
[ROW][C]149[/C][C]0.2287[/C][C]-0.1432[/C][C]0.0748[/C][C]257.2387[/C][C]89.9201[/C][C]9.4826[/C][/ROW]
[ROW][C]150[/C][C]0.2316[/C][C]-0.124[/C][C]0.0803[/C][C]192.5878[/C][C]101.3276[/C][C]10.0662[/C][/ROW]
[ROW][C]151[/C][C]0.2342[/C][C]0.0916[/C][C]0.0814[/C][C]104.8747[/C][C]101.6823[/C][C]10.0838[/C][/ROW]
[ROW][C]152[/C][C]0.2366[/C][C]0.2[/C][C]0.0922[/C][C]498.8266[/C][C]137.7863[/C][C]11.7382[/C][/ROW]
[ROW][C]153[/C][C]0.239[/C][C]0.1919[/C][C]0.1005[/C][C]458.5456[/C][C]164.5163[/C][C]12.8264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195135&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195135&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
1420.0974-0.0528040.403100
1430.153-0.08040.066689.091364.74728.0466
1440.1858-0.08140.071588.253172.58258.5195
1450.2042-0.02670.06039.267856.75387.5335
1460.2146-0.02810.053910.097147.42256.8864
1470.221-0.07660.057774.407751.927.2056
1480.2254-0.10930.065150.601766.01748.1251
1490.2287-0.14320.0748257.238789.92019.4826
1500.2316-0.1240.0803192.5878101.327610.0662
1510.23420.09160.0814104.8747101.682310.0838
1520.23660.20.0922498.8266137.786311.7382
1530.2390.19190.1005458.5456164.516312.8264


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = -0.5 ; par3 = 1 ; par4 = 1 ; par5 = 1 ; par6 = 1 ; par7 = 1 ; par8 = 1 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = -0.5 ; par3 = 1 ; par4 = 1 ; par5 = 1 ; par6 = 1 ; par7 = 1 ; par8 = 1 ; par9 = 1 ; 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')