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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 computationWed, 09 Dec 2009 12:29:01 -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/09/t1260389082z3p39l7opfpg2nm.htm/, Retrieved Mon, 29 Apr 2024 14:27:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65192, Retrieved Mon, 29 Apr 2024 14:27:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [] [2009-12-06 10:44:58] [1e83ffa964db6f7ea6ccc4e7b5acbbff]
-   PD    [ARIMA Forecasting] [ws 10 deel 2 prblm] [2009-12-09 19:29:01] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
- RMP       [(Partial) Autocorrelation Function] [WS 10 forum ACF] [2009-12-11 10:14:54] [83058a88a37d754675a5cd22dab372fc]
- RMP       [Standard Deviation-Mean Plot] [WS 10 Forum SMP] [2009-12-11 10:18:43] [83058a88a37d754675a5cd22dab372fc]
- RMP       [(Partial) Autocorrelation Function] [] [2009-12-11 10:27:22] [83058a88a37d754675a5cd22dab372fc]
- RM        [ARIMA Backward Selection] [WS 10 Forum ARIMA] [2009-12-11 10:33:01] [83058a88a37d754675a5cd22dab372fc]
-   P       [ARIMA Forecasting] [ws 10 deel 2 arim...] [2009-12-12 09:45:03] [134dc66689e3d457a82860db6471d419]
-    D        [ARIMA Forecasting] [WS 10 ] [2009-12-12 11:53:20] [3425351e86519d261a643e224a0c8ee1]
-   PD          [ARIMA Forecasting] [] [2009-12-19 16:24:22] [3425351e86519d261a643e224a0c8ee1]
-   P             [ARIMA Forecasting] [] [2009-12-20 10:24:37] [3425351e86519d261a643e224a0c8ee1]
-   PD              [ARIMA Forecasting] [ARIMA forecasting] [2009-12-21 15:59:35] [76ab39dc7a55316678260825bd5ad46c]
-   PD        [ARIMA Forecasting] [Forecasting] [2009-12-14 15:18:19] [24c4941ee50deadff4640c9c09cc70cb]
-   P         [ARIMA Forecasting] [Paper ARIMA F IGP] [2009-12-14 21:07:10] [134dc66689e3d457a82860db6471d419]
-   PD          [ARIMA Forecasting] [Paper ARIMA F ICP] [2009-12-15 20:21:11] [134dc66689e3d457a82860db6471d419]
-   P           [ARIMA Forecasting] [Paper ARIMA F IGP 12] [2009-12-15 20:24:19] [134dc66689e3d457a82860db6471d419]
- R PD            [ARIMA Forecasting] [Paper arima forec...] [2009-12-23 22:37:42] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
- R PD            [ARIMA Forecasting] [Paper arima forec...] [2009-12-23 23:25:41] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
-   P         [ARIMA Forecasting] [workshop 10] [2009-12-18 17:50:14] [28d531aeb5ea2ff1b676cbab66947a19]
- R P       [ARIMA Forecasting] [ws 10 deel 2 arim...] [2009-12-12 09:39:49] [134dc66689e3d457a82860db6471d419]
-    D        [ARIMA Forecasting] [WS 10] [2009-12-12 11:57:08] [3425351e86519d261a643e224a0c8ee1]
- R P         [ARIMA Forecasting] [] [2009-12-12 19:34:20] [b98453cac15ba1066b407e146608df68]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.01
103.84
104.48
95.43
104.80
108.64
105.65
108.42
115.35
113.64
115.24
100.33
101.29
104.48
99.26
100.11
103.52
101.18
96.39
97.56
96.39
85.10
79.77
79.13
80.84
82.75
92.55
96.60
96.92
95.32
98.52
100.22
104.91
103.10
97.13
103.42
111.72
118.11
111.62
100.22
102.03
105.76
107.68
110.77
105.44
112.26
114.07
117.90
124.72
126.42
134.73
135.79
143.36
140.37
144.74
151.98
150.92
163.38
154.43
146.66
157.95
162.10
180.42
179.57
171.58
185.43
190.64
203.00
202.36
193.41
186.17
192.24
209.60
206.41
209.82
230.37
235.80
232.07
244.64
242.19
217.48
209.39
211.73
221.00
203.11
214.71
224.19
238.04
238.36
246.24
259.87
249.97
266.48
282.98
306.31
301.73
314.62
332.62
355.51
370.32
408.13
433.58
440.51
386.29
342.84
254.97
203.42
170.09
174.03
167.85
177.01
188.19
211.20
240.91
230.26
251.25
241.66

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
R Engine error message
Error in plot.window(...) : need finite 'ylim' values
Calls: plot -> plot.default -> localWindow -> plot.window
Execution halted


\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
R Engine error message & 
Error in plot.window(...) : need finite 'ylim' values
Calls: plot -> plot.default -> localWindow -> plot.window
Execution halted

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65192&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]
[ROW][C]R Engine error message[/C][C]
Error in plot.window(...) : need finite 'ylim' values
Calls: plot -> plot.default -> localWindow -> plot.window
Execution halted

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65192&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65192&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
R Engine error message
Error in plot.window(...) : need finite 'ylim' values
Calls: plot -> plot.default -> localWindow -> plot.window
Execution halted


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 24 ; par2 = -0.7 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 24 ; par2 = -0.7 ; par3 = 2 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; 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,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 <- 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.se <- array(0, dim=fx)
perf.mse <- 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$pred[i]
perf.mape[i] = perf.mape[i] + abs(perf.pe[i])
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
perf.mse[i] = perf.mse[i] + perf.se[i]
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 = perf.mape / fx
perf.mse = perf.mse / fx
perf.rmse = sqrt(perf.mse)
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[1:12] <- 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.mape[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse[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')