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Data:
8.9 8.8 8.3 7.5 7.2 7.4 8.8 9.3 9.3 8.7 8.2 8.3 8.5 8.6 8.5 8.2 8.1 7.9 8.6 8.7 8.7 8.5 8.4 8.5 8.7 8.7 8.6 8.5 8.3 8 8.2 8.1 8.1 8 7.9 7.9 8 8 7.9 8 7.7 7.2 7.5 7.3 7 7 7 7.2 7.3 7.1 6.8 6.4 6.1 6.5 7.7 7.9 7.5 6.9 6.6 6.9
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
36
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
1
1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Degree of non-seasonal differencing (d)
1
0
1
2
Degree of seasonal differencing (D)
1
0
1
2
Seasonality
12
12
1
2
3
4
6
12
CI type
MA
White Noise
MA
Confidence Interval
Use logarithms with this base
(overrules the Box-Cox lambda parameter)
(?)
Chart options
R Code
if #par1 ## 'Default'# { par1 # 10*log10#length#x## } else { par1 <- as.numeric#par1# } par2 <- as.numeric#par2# par3 <- as.numeric#par3# par4 <- as.numeric#par4# par5 <- as.numeric#par5# if #par6 ## 'White Noise'# par6 <- 'white' else par6 <- 'ma' par7 <- as.numeric#par7# if #par2 ## 0# { x <- log#x# } else { x <- #x ^ par2 - 1# / par2 } if #par3 > 0# x <- diff#x,lag#1,difference#par3# if #par4 > 0# x <- diff#x,lag#par5,difference#par4# bitmap#file#'pic1.png'# racf <- acf#x, par1, main#'Autocorrelation', xlab#'time lag', ylab#'ACF', ci.type#par6, ci#par7, sub#paste#'#lambda#',par2,', d#',par3,', D#',par4,', CI#', par7, ', CI type#',par6,'#',sep#''## dev.off## bitmap#file#'pic2.png'# rpacf <- pacf#x,par1,main#'Partial Autocorrelation',xlab#'lags',ylab#'PACF'# dev.off## lengthx <- length#x# sqrtn <- sqrt#lengthx# load#file#'createtable'# a<-table.start## a<-table.row.start#a# a<-table.element#a,'Autocorrelation Function',4,TRUE# a<-table.row.end#a# a<-table.row.start#a# a<-table.element#a,'Time lag k',header#TRUE# a<-table.element#a,hyperlink#'http://www.xycoon.com/basics.htm','ACF#k#','click here for more information about the Autocorrelation Function'#,header#TRUE# a<-table.element#a,'T-STAT',header#TRUE# a<-table.element#a,'P-value',header#TRUE# a<-table.row.end#a# for #i in 2:#par1+1## { a<-table.row.start#a# a<-table.element#a,i-1,header#TRUE# a<-table.element#a,round#myacf[i],6## mytstat <- myacf[i]*sqrtn a<-table.element#a,round#mytstat,4## a<-table.element#a,round#1-pt#abs#mytstat#,lengthx#,6## 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,'Partial Autocorrelation Function',4,TRUE# a<-table.row.end#a# a<-table.row.start#a# a<-table.element#a,'Time lag k',header#TRUE# a<-table.element#a,hyperlink#'http://www.xycoon.com/basics.htm','PACF#k#','click here for more information about the Partial Autocorrelation Function'#,header#TRUE# a<-table.element#a,'T-STAT',header#TRUE# a<-table.element#a,'P-value',header#TRUE# a<-table.row.end#a# for #i in 1:par1# { a<-table.row.start#a# a<-table.element#a,i,header#TRUE# a<-table.element#a,round#mypacf[i],6## mytstat <- mypacf[i]*sqrtn a<-table.element#a,round#mytstat,4## a<-table.element#a,round#1-pt#abs#mytstat#,lengthx#,6## a<-table.row.end#a# } a<-table.end#a# table.save#a,file#'mytable1.tab'#
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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