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Data:
12.9 12.2 12.8 7.4 6.7 12.6 14.8 13.3 11.1 8.2 11.4 6.4 10.6 12.0 6.3 11.3 11.9 9.3 9.6 10.0 6.4 13.8 10.8 13.8 11.7 10.9 16.1 13.4 9.9 11.5 8.3 11.7 9.0 9.7 10.8 10.3 10.4 12.7 9.3 11.8 5.9 11.4 13.0 10.8 12.3 11.3 11.8 7.9 12.7 12.3 11.6 6.7 10.9 12.1 13.3 10.1 5.7 14.3 8.0 13.3 9.3 12.5 7.6 15.9 9.2 9.1 11.1 13.0 14.5 12.2 12.3 11.4 8.8 14.6 12.6 13.0 12.6 13.2 9.9 7.7 10.5 13.4 10.9 4.3 10.3 11.8 11.2 11.4 8.6 13.2 12.6 5.6 9.9 8.8 7.7 9.0 7.3 11.4 13.6 7.9 10.7 10.3 8.3 9.6 14.2 8.5 13.5 4.9 6.4 9.6 11.6 11.1 4.35 12.7 18.1 17.85 16.6 12.6 17.1 19.1 16.1 13.35 18.4 14.7 10.6 12.6 16.2 13.6 18.9 14.1 14.5 16.15 14.75 14.8 12.45 12.65 17.35 8.6 18.4 16.1 11.6 17.75 15.25 17.65 16.35 17.65 13.6 14.35 14.75 18.25 9.9 16 18.25 16.85 14.6 13.85 18.95 15.6 14.85 11.75 18.45 15.9 17.1 16.1 19.9 10.95 18.45 15.1 15 11.35 15.95 18.1 14.6 15.4 15.4 17.6 13.35 19.1 15.35 7.6 13.4 13.9 19.1 15.25 12.9 16.1 17.35 13.15 12.15 12.6 10.35 15.4 9.6 18.2 13.6 14.85 14.75 14.1 14.9 16.25 19.25 13.6 13.6 15.65 12.75 14.6 9.85 12.65 19.2 16.6 11.2 15.25 11.9 13.2 16.35 12.4 15.85 18.15 11.15 15.65 17.75 7.65 12.35 15.6 19.3 15.2 17.1 15.6 18.4 19.05 18.55 19.1 13.1 12.85 9.5 4.5 11.85 13.6 11.7 12.4 13.35 11.4 14.9 19.9 11.2 14.6 17.6 14.05 16.1 13.35 11.85 11.95 14.75 15.15 13.2 16.85 7.85 7.7 12.6 7.85 10.95 12.35 9.95 14.9 16.65 13.4 13.95 15.7 16.85 10.95 15.35 12.2 15.1 17.75 15.2 14.6 16.65 8.1
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
60
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 (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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Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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