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Data:
7.5 2.5 6.0 6.5 1.0 1.0 5.5 8.5 6.5 4.5 2.0 5.0 0.5 5.0 5.0 2.5 5.0 5.5 3.5 3.0 4.0 0.5 6.5 4.5 7.5 5.5 4.0 7.5 7.0 4.0 5.5 2.5 5.5 0.5 3.5 2.5 4.5 4.5 4.5 6.0 2.5 5.0 0.0 5.0 6.5 5.0 6.0 4.5 5.5 1.0 7.5 6.0 5.0 1.0 5.0 6.5 7.0 4.5 0.0 8.5 3.5 7.5 3.5 6.0 1.5 9.0 3.5 3.5 4.0 6.5 7.5 6.0 5.0 5.5 3.5 7.5 1.0 6.5 6.5 6.5 7.0 3.5 1.5 4.0 7.5 4.5 0.0 3.5 5.5 5.0 4.5 2.5 7.5 7.0 0.0 4.5 3.0 1.5 3.5 2.5 5.5 8.0 1.0 5.0 4.5 3.0 3.0 8.0 2.5 7.0 0.0 1.0 3.5 5.5 5.5 0.5 7.5 9 9.5 8.5 7 8 10 7 8.5 9 9.5 4 6 8 5.5 9.5 7.5 7 7.5 8 7 7 6 10 2.5 9 8 6 8.5 6 9 8 8 9 5.5 5 7 5.5 9 2 8.5 9 8.5 9 7.5 10 9 7.5 6 10.5 8.5 8 10 10.5 6.5 9.5 8.5 7.5 5 8 10 7 7.5 7.5 9.5 6 10 7 3 6 7 10 7 3.5 8 10 5.5 6 6.5 6.5 8.5 4 9.5 8 8.5 5.5 7 9 8 10 8 6 8 5 9 4.5 8.5 7 9.5 8.5 7.5 7.5 5 7 8 5.5 8.5 7.5 9.5 7 8 8.5 3.5 6.5 6.5 10.5 8.5 8 10 10 9.5 9 10 7.5 4.5 4.5 0.5 6.5 4.5 5.5 5 6 4 8 10.5 8.5 6.5 8 8.5 5.5 7 5 3.5 5 9 8.5 5 9.5 3 1.5 6 0.5 6.5 7.5 4.5 8 9 7.5 8.5 7 9.5 6.5 9.5 6 8 9.5 8 8 9
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
12
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
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)
0
1
2
Degree of seasonal differencing (D)
0
1
2
Seasonality
12
1
2
3
4
6
12
CI type
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 (par8 != '') par8 <- as.numeric(par8) ox <- x if (par8 == '') { if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } } else { x <- log(x,base=par8) } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='picts.png') op <- par(mfrow=c(2,1)) plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value') if (par8=='') { mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } else { mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } plot(x,type='l', main=mytitle,xlab='time',ylab='value') par(op) dev.off() bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub) 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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