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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_exponentialsmoothing.wasp
Title produced by softwareExponential Smoothing
Date of computationSun, 14 Dec 2014 21:52:03 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/14/t1418593942emcs7v02h1gt7cb.htm/, Retrieved Thu, 16 May 2024 17:55:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267903, Retrieved Thu, 16 May 2024 17:55:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact458

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [KUL paper CD vrou...] [2014-12-14 20:44:21] [bb1b6762b7e5624d262776d3f7139d34]
- RMPD  [Spectral Analysis] [KUl paper SP vrou...] [2014-12-14 20:50:58] [bb1b6762b7e5624d262776d3f7139d34]
- R       [Spectral Analysis] [KUL paper vrouw s...] [2014-12-14 20:52:17] [bb1b6762b7e5624d262776d3f7139d34]
- R P       [Spectral Analysis] [KUl paper SP vrou...] [2014-12-14 21:20:19] [bb1b6762b7e5624d262776d3f7139d34]
- RMP         [Classical Decomposition] [KUl paper CD test ] [2014-12-14 21:36:33] [bb1b6762b7e5624d262776d3f7139d34]
- RM              [Exponential Smoothing] [Kul paper ES2 vrouw] [2014-12-14 21:52:03] [8568a324fefbb8dbb43f697bfa8d1be6] [Current]
- R                 [Exponential Smoothing] [Kul paper ES2 vrouw2] [2014-12-14 21:53:13] [bb1b6762b7e5624d262776d3f7139d34]
- RM                [ARIMA Backward Selection] [Kul paper ARIMA 1...] [2014-12-14 22:30:59] [bb1b6762b7e5624d262776d3f7139d34]
- R  D                [ARIMA Backward Selection] [Kul paper ARIMA 1...] [2014-12-14 22:36:50] [bb1b6762b7e5624d262776d3f7139d34]
- R                     [ARIMA Backward Selection] [Kul paper ARIMA v...] [2014-12-14 22:38:10] [bb1b6762b7e5624d262776d3f7139d34]
-   P                     [ARIMA Backward Selection] [Kul paper berkeni...] [2014-12-18 15:19:26] [bb1b6762b7e5624d262776d3f7139d34]
- RM D                  [Central Tendency] [KUL paper central...] [2014-12-14 23:14:31] [bb1b6762b7e5624d262776d3f7139d34]
- RM D                  [Skewness and Kurtosis Test] [Paper KUL arima s...] [2014-12-14 23:25:23] [bb1b6762b7e5624d262776d3f7139d34]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7.5
NA
6.5
NA
NA
NA
8.5
NA
NA
NA
NA
NA
NA
5
2.5
5
NA
3.5
NA
4
NA
NA
4.5
NA
NA
NA
NA
7
NA
5.5
2.5
5.5
NA
NA
NA
NA
4.5
NA
NA
5
NA
NA
NA
NA
NA
4.5
NA
NA
7.5
NA
NA
NA
NA
NA
NA
NA
0
NA
3.5
NA
NA
6
1.5
NA
3.5
NA
4
NA
NA
6
5
5.5
3.5
NA
6.5
6.5
NA
7
3.5
NA
4
7.5
4.5
NA
3.5
NA
NA
4.5
2.5
7.5
NA
NA
NA
3
NA
3.5
NA
NA
NA
NA
NA
4.5
NA
NA
NA
2.5
7
0
1
3.5
5.5
NA
NA
NA
NA
NA
8.5
NA
NA
10
NA
8.5
9
NA
NA
NA
NA
NA
NA
NA
NA
7.5
NA
NA
NA
NA
NA
NA
9
NA
NA
NA
NA
NA
8
9
NA
7
5.5
NA
2
NA
NA
8.5
NA
NA
NA
9
7.5
6
10.5
NA
8
NA
10.5
NA
9.5
NA
7.5
5
NA
10
NA
NA
NA
NA
NA
10
NA
3
6
7
NA
7
NA
8
10
5.5
6
NA
NA
NA
NA
9.5
8
NA
5.5
7
9
8
NA
NA
6
8
NA
9
NA
NA
9.5
NA
NA
NA
5
7
8
NA
NA
NA
NA
8
8.5
3.5
NA
NA
10.5
8.5
8
NA
NA
9.5
9
10
NA
NA
NA
NA
6.5
NA
NA
NA
6
4
NA
10.5
NA
NA
8.5
NA
7
NA
NA
5
NA
8.5
NA
9.5
NA
1.5
6
NA
NA
7.5
NA
NA
9
NA
8.5
7
NA
NA
9.5
NA
8
9.5
NA
8
9

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'Herman Ole Andreas Wold' @ wold.wessa.net
R Engine error message
Error in hw(p[1L], p[2L], gamma) : 
  NA/NaN/Inf in foreign function call (arg 1)
Calls: HoltWinters -> optim ->  -> fn -> hw
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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Engine error message & 
Error in hw(p[1L], p[2L], gamma) : 
  NA/NaN/Inf in foreign function call (arg 1)
Calls: HoltWinters -> optim ->  -> fn -> hw
Execution halted

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267903&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Engine error message[/C][C]
Error in hw(p[1L], p[2L], gamma) : 
  NA/NaN/Inf in foreign function call (arg 1)
Calls: HoltWinters -> optim ->  -> fn -> hw
Execution halted

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267903&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267903&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'Herman Ole Andreas Wold' @ wold.wessa.net
R Engine error message
Error in hw(p[1L], p[2L], gamma) : 
  NA/NaN/Inf in foreign function call (arg 1)
Calls: HoltWinters -> optim ->  -> fn -> hw
Execution halted


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Double ; par3 = additive ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par1, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$alpha)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,fit$beta)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'gamma',header=TRUE)
a<-table.element(a,fit$gamma)
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,'Interpolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nxmK) {
a<-table.row.start(a)
a<-table.element(a,i+K,header=TRUE)
a<-table.element(a,x[i+K])
a<-table.element(a,fit$fitted[i,'xhat'])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Forecast',header=TRUE)
a<-table.element(a,'95% Lower Bound',header=TRUE)
a<-table.element(a,'95% Upper Bound',header=TRUE)
a<-table.row.end(a)
for (i in 1:np) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,p[i,'fit'])
a<-table.element(a,p[i,'lwr'])
a<-table.element(a,p[i,'upr'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')