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Data:
20 25 15 15 25 25 25 21 30 25 20 40 13 30 25 20 25 20 25 20 20 15 15 12 20 5 20 15 25 22 20 22 25 20 20 35 30 25 20 20 20 25 25 15 20 35 25 25 30 23 10 22 25 25 22 30 20 25 25 22 25 25 25 22 25 12 18 20 20 22 30 25 22 20 50 30 25 20 30 22 25 30 22 25 22 22 25 25 25 20 22 15 20 30 20 25 30 35 22 12 30 15 10 30 9 25 20 20 35 25 35 30 12 25 15 25 25 20 20 6 15 40 20 40 25 25 20 15 15 22 24 22 20 25 25 25 35 40 20 22 22 20 25 25 18 25 20 25 30 20 22 35 22 25 25 25 25 22 23 35 15 25 18 22 25 25 28 30 20 25 25 30 22 30 10 10 25 20 22 25 25 15 22 25 25 28 22 30 25 20 25 25 20 30 20 30 50 19 20 28 20 25 35 25 25 15 16 20 20 25 30 20 25 25 25 20 20 25 25 30 22 20 25 25 18 18 20 25 25 30 25 20 25 20 20 20 22 18 22 20 15 25 25 20 25 15 22 25 25 15 12 25 30 22 15 22 25 12 18 30 25 25 40 24 25 15 25 20 25 25 25 20 30 20 25 30 22 25 25 25 50 19 50 25 35 20 20 20 20 20 25 25 25 20 20 20 20 25 18 25 22 22 30 30 8 20 25 30 50 22 20 10 25 25 25 25 18 25 20 25 30 18 20 25 22 22 20 20 25 20 20 20 20 25 20 10 20 25 30 25 50 30 30 50 15 25 25 22 20 22 30 25 18 22 22 30 40 25 20 10 20 9 15 20 15 20 30 12 15 12 20 15 12 25 20 25 25 25 30 20 25 15 15 22 10 15 10 20 25 20 20 38 20 20 20 40 25 25 30 25 10 20 25 12 15 25 20 22 22 20 25 25 25 15 40 20 20 16 25 15 20 25 20 30 50 20 25 20 30 30 25 25 12 25 25 25 20 20 20 15 20 25 15 25 50 30 20 20 25 12 15 20 20 35 22 15 18 30 22 12 12 20 20 15 25 15 20 20 25 18 30 20 25 25 25 20 20 25 20 22 15 15 22 20 10 25 20 20 15 12 20 5 20 15 15 25 25 25 15 25 22 25 20 18 22 25 35 25 25 25 35 30 22 30 50 15 25 24 20 25 25 25 12 15 22 25 25 25 25 15 20 20 15 35 30 20 22 65 20 25 22 20 25 25 20 25 15 20 12 15 10 25 15 30 35 25 25 25 25 25 40 40 25 25 20 25 25 22 25 30 25 25 30 25 25 30 25 25 20 22 22 20 25 22 25 22 40 25 25 25 22 20 35 20 35 25 22 25 25 25 25 25 40 25 30 25 20 25 25 30 22 22 20 15 15 25 25 20 20 15 25 15 20 22 25 15 15 18 5 15 25 18 40 25 25 20 30 20 25 25 25 22 22 25 25 30 25 25 25 25 20 20 25 25 25 25 20 30 25 22 30 20 20 30 25 25 30 20 25 25 24 25 30 18 15 22 22 25 22 22 25 15 20 22 18 35 20 20 20 25 25 30 15 25 22 26 25 20 25 25 25 22 25 25 20 22 30 15 30 25 20 25 25 35 22 20 25 20 20 18 20 22 25 10 20 25 20 20 30 25 20 15 20 25 10 20 25 22 22 25 25 15 25 20 10 25 16 25 35 25 15 25 25 30 25 10 22 20 25 20 20 25 22 18 30 19 25 20 25 20 25 20 22 12 30 12 22 25 25 25 25 30 30 10 22 22 25 20 22 20 25 20 15 25 20 25 20 30 15 40 25 20 22 22 30 20 40 20 25 20 25 20 50 50 25 25 40 30 22 30 20 25 25 30 25 25 20 18 18 28 25 22 15 40 40 12 12 18 12 25 26 18 25 22 15 25 15 15 15 25 15 12 22 20 20 25 20 12 9 15 12 15 25 20 20 15 15 30 21 25 22 22 50 15 25 15 25 22 18 50 20 50 20 20 30 25 20 22 25 50 40 25 25 25 25 30 40 25 30 20
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
Default
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)
0
0
1
2
Seasonality
12
12
1
2
3
4
6
12
CI type
White Noise
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() (myacf <- c(racf$acf)) (mypacf <- c(rpacf$acf)) 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')
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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