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Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationFri, 07 Dec 2012 09:02:03 -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/Dec/07/t1354888932gx0qqi9b3pr1o2e.htm/, Retrieved Thu, 28 Mar 2024 09:16:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197378, Retrieved Thu, 28 Mar 2024 09:16:54 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-10-30 15:05:45] [d2c1a12335a0e7c18f8727e39be21dbc]
- RMPD    [ARIMA Forecasting] [] [2012-12-07 14:02:03] [a5d65c007476aeb95d503c7a121a195d] [Current]
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Dataseries X:
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




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=197378&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=197378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197378&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 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[60])
4869-------
4960-------
5056-------
5158-------
5250-------
5351-------
5453-------
5537-------
5622-------
5755-------
5870-------
5962-------
6058-------
61396037.385682.61440.03440.56880.50.5688
62495633.385678.61440.2720.92970.50.4312
63585835.385680.61440.50.78230.50.5
64475027.385672.61440.39740.2440.50.244
65425128.385673.61440.21770.63560.50.272
66625330.385675.61440.21770.82980.50.3324
67393714.385659.61440.43120.01510.50.0344
684022-0.614444.61440.05940.07030.59e-04
69725532.385677.61440.07030.90320.50.3974
70707047.385692.61440.50.43120.50.8508
71546239.385684.61440.2440.2440.50.6356
72655835.385680.61440.2720.63560.50.5

\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
48 & 69 & - & - & - & - & - & - & - \tabularnewline
49 & 60 & - & - & - & - & - & - & - \tabularnewline
50 & 56 & - & - & - & - & - & - & - \tabularnewline
51 & 58 & - & - & - & - & - & - & - \tabularnewline
52 & 50 & - & - & - & - & - & - & - \tabularnewline
53 & 51 & - & - & - & - & - & - & - \tabularnewline
54 & 53 & - & - & - & - & - & - & - \tabularnewline
55 & 37 & - & - & - & - & - & - & - \tabularnewline
56 & 22 & - & - & - & - & - & - & - \tabularnewline
57 & 55 & - & - & - & - & - & - & - \tabularnewline
58 & 70 & - & - & - & - & - & - & - \tabularnewline
59 & 62 & - & - & - & - & - & - & - \tabularnewline
60 & 58 & - & - & - & - & - & - & - \tabularnewline
61 & 39 & 60 & 37.3856 & 82.6144 & 0.0344 & 0.5688 & 0.5 & 0.5688 \tabularnewline
62 & 49 & 56 & 33.3856 & 78.6144 & 0.272 & 0.9297 & 0.5 & 0.4312 \tabularnewline
63 & 58 & 58 & 35.3856 & 80.6144 & 0.5 & 0.7823 & 0.5 & 0.5 \tabularnewline
64 & 47 & 50 & 27.3856 & 72.6144 & 0.3974 & 0.244 & 0.5 & 0.244 \tabularnewline
65 & 42 & 51 & 28.3856 & 73.6144 & 0.2177 & 0.6356 & 0.5 & 0.272 \tabularnewline
66 & 62 & 53 & 30.3856 & 75.6144 & 0.2177 & 0.8298 & 0.5 & 0.3324 \tabularnewline
67 & 39 & 37 & 14.3856 & 59.6144 & 0.4312 & 0.0151 & 0.5 & 0.0344 \tabularnewline
68 & 40 & 22 & -0.6144 & 44.6144 & 0.0594 & 0.0703 & 0.5 & 9e-04 \tabularnewline
69 & 72 & 55 & 32.3856 & 77.6144 & 0.0703 & 0.9032 & 0.5 & 0.3974 \tabularnewline
70 & 70 & 70 & 47.3856 & 92.6144 & 0.5 & 0.4312 & 0.5 & 0.8508 \tabularnewline
71 & 54 & 62 & 39.3856 & 84.6144 & 0.244 & 0.244 & 0.5 & 0.6356 \tabularnewline
72 & 65 & 58 & 35.3856 & 80.6144 & 0.272 & 0.6356 & 0.5 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197378&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]48[/C][C]69[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]60[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]56[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]51[/C][C]58[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]52[/C][C]50[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]54[/C][C]53[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]55[/C][C]37[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]57[/C][C]55[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]58[/C][C]70[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/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]60[/C][C]37.3856[/C][C]82.6144[/C][C]0.0344[/C][C]0.5688[/C][C]0.5[/C][C]0.5688[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]56[/C][C]33.3856[/C][C]78.6144[/C][C]0.272[/C][C]0.9297[/C][C]0.5[/C][C]0.4312[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]58[/C][C]35.3856[/C][C]80.6144[/C][C]0.5[/C][C]0.7823[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]50[/C][C]27.3856[/C][C]72.6144[/C][C]0.3974[/C][C]0.244[/C][C]0.5[/C][C]0.244[/C][/ROW]
[ROW][C]65[/C][C]42[/C][C]51[/C][C]28.3856[/C][C]73.6144[/C][C]0.2177[/C][C]0.6356[/C][C]0.5[/C][C]0.272[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]53[/C][C]30.3856[/C][C]75.6144[/C][C]0.2177[/C][C]0.8298[/C][C]0.5[/C][C]0.3324[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]37[/C][C]14.3856[/C][C]59.6144[/C][C]0.4312[/C][C]0.0151[/C][C]0.5[/C][C]0.0344[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]22[/C][C]-0.6144[/C][C]44.6144[/C][C]0.0594[/C][C]0.0703[/C][C]0.5[/C][C]9e-04[/C][/ROW]
[ROW][C]69[/C][C]72[/C][C]55[/C][C]32.3856[/C][C]77.6144[/C][C]0.0703[/C][C]0.9032[/C][C]0.5[/C][C]0.3974[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]70[/C][C]47.3856[/C][C]92.6144[/C][C]0.5[/C][C]0.4312[/C][C]0.5[/C][C]0.8508[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]62[/C][C]39.3856[/C][C]84.6144[/C][C]0.244[/C][C]0.244[/C][C]0.5[/C][C]0.6356[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]58[/C][C]35.3856[/C][C]80.6144[/C][C]0.272[/C][C]0.6356[/C][C]0.5[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197378&T=1

As an alternative you can also use a 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])
4869-------
4960-------
5056-------
5158-------
5250-------
5351-------
5453-------
5537-------
5622-------
5755-------
5870-------
5962-------
6058-------
61396037.385682.61440.03440.56880.50.5688
62495633.385678.61440.2720.92970.50.4312
63585835.385680.61440.50.78230.50.5
64475027.385672.61440.39740.2440.50.244
65425128.385673.61440.21770.63560.50.272
66625330.385675.61440.21770.82980.50.3324
67393714.385659.61440.43120.01510.50.0344
684022-0.614444.61440.05940.07030.59e-04
69725532.385677.61440.07030.90320.50.3974
70707047.385692.61440.50.43120.50.8508
71546239.385684.61440.2440.2440.50.6356
72655835.385680.61440.2720.63560.50.5







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
610.1923-0.35044100
620.206-0.1250.23754924515.6525
630.198900.15830163.333312.7802
640.2308-0.060.13379124.7511.1692
650.2262-0.17650.14238111610.7703
660.21770.16980.146981110.166710.496
670.31180.05410.13364959.7468
680.52450.81820.2192324123.62511.1187
690.20980.30910.229228914211.9164
700.164800.20630127.811.3049
710.1861-0.1290.19926412211.0454
720.19890.12070.192749115.916710.7665

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
61 & 0.1923 & -0.35 & 0 & 441 & 0 & 0 \tabularnewline
62 & 0.206 & -0.125 & 0.2375 & 49 & 245 & 15.6525 \tabularnewline
63 & 0.1989 & 0 & 0.1583 & 0 & 163.3333 & 12.7802 \tabularnewline
64 & 0.2308 & -0.06 & 0.1337 & 9 & 124.75 & 11.1692 \tabularnewline
65 & 0.2262 & -0.1765 & 0.1423 & 81 & 116 & 10.7703 \tabularnewline
66 & 0.2177 & 0.1698 & 0.1469 & 81 & 110.1667 & 10.496 \tabularnewline
67 & 0.3118 & 0.0541 & 0.1336 & 4 & 95 & 9.7468 \tabularnewline
68 & 0.5245 & 0.8182 & 0.2192 & 324 & 123.625 & 11.1187 \tabularnewline
69 & 0.2098 & 0.3091 & 0.2292 & 289 & 142 & 11.9164 \tabularnewline
70 & 0.1648 & 0 & 0.2063 & 0 & 127.8 & 11.3049 \tabularnewline
71 & 0.1861 & -0.129 & 0.1992 & 64 & 122 & 11.0454 \tabularnewline
72 & 0.1989 & 0.1207 & 0.1927 & 49 & 115.9167 & 10.7665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197378&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]0.1923[/C][C]-0.35[/C][C]0[/C][C]441[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]62[/C][C]0.206[/C][C]-0.125[/C][C]0.2375[/C][C]49[/C][C]245[/C][C]15.6525[/C][/ROW]
[ROW][C]63[/C][C]0.1989[/C][C]0[/C][C]0.1583[/C][C]0[/C][C]163.3333[/C][C]12.7802[/C][/ROW]
[ROW][C]64[/C][C]0.2308[/C][C]-0.06[/C][C]0.1337[/C][C]9[/C][C]124.75[/C][C]11.1692[/C][/ROW]
[ROW][C]65[/C][C]0.2262[/C][C]-0.1765[/C][C]0.1423[/C][C]81[/C][C]116[/C][C]10.7703[/C][/ROW]
[ROW][C]66[/C][C]0.2177[/C][C]0.1698[/C][C]0.1469[/C][C]81[/C][C]110.1667[/C][C]10.496[/C][/ROW]
[ROW][C]67[/C][C]0.3118[/C][C]0.0541[/C][C]0.1336[/C][C]4[/C][C]95[/C][C]9.7468[/C][/ROW]
[ROW][C]68[/C][C]0.5245[/C][C]0.8182[/C][C]0.2192[/C][C]324[/C][C]123.625[/C][C]11.1187[/C][/ROW]
[ROW][C]69[/C][C]0.2098[/C][C]0.3091[/C][C]0.2292[/C][C]289[/C][C]142[/C][C]11.9164[/C][/ROW]
[ROW][C]70[/C][C]0.1648[/C][C]0[/C][C]0.2063[/C][C]0[/C][C]127.8[/C][C]11.3049[/C][/ROW]
[ROW][C]71[/C][C]0.1861[/C][C]-0.129[/C][C]0.1992[/C][C]64[/C][C]122[/C][C]11.0454[/C][/ROW]
[ROW][C]72[/C][C]0.1989[/C][C]0.1207[/C][C]0.1927[/C][C]49[/C][C]115.9167[/C][C]10.7665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197378&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197378&T=2

As an alternative you can also use a QR Code:  

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

Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
610.1923-0.35044100
620.206-0.1250.23754924515.6525
630.198900.15830163.333312.7802
640.2308-0.060.13379124.7511.1692
650.2262-0.17650.14238111610.7703
660.21770.16980.146981110.166710.496
670.31180.05410.13364959.7468
680.52450.81820.2192324123.62511.1187
690.20980.30910.229228914211.9164
700.164800.20630127.811.3049
710.1861-0.1290.19926412211.0454
720.19890.12070.192749115.916710.7665



Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; 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 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')