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Data:
12.90 7.40 12.20 12.80 7.40 6.70 12.60 14.80 13.30 11.10 8.20 11.40 6.40 10.60 12.00 6.30 11.30 11.90 9.30 9.60 10.00 6.40 13.80 10.80 13.80 11.70 10.90 16.10 13.40 9.90 11.50 8.30 11.70 6.10 9.00 9.70 10.80 10.30 10.40 12.70 9.30 11.80 5.90 11.40 13.00 10.80 12.30 11.30 11.80 7.90 12.70 12.30 11.60 6.70 10.90 12.10 13.30 10.10 5.70 14.30 8.00 13.30 9.30 12.50 7.60 15.90 9.20 9.10 11.10 13.00 14.50 12.20 12.30 11.40 8.80 14.60 7.30 12.60 13.00 12.60 13.20 9.90 7.70 10.50 13.40 10.90 4.30 10.30 11.80 11.20 11.40 8.60 13.20 12.60 5.60 9.90 8.80 7.70 9.00 7.30 11.40 13.60 7.90 10.70 10.30 8.30 9.60 14.20 8.50 13.50 4.90 6.40 9.60 11.60 11.10 4.35 12.70 18.10 17.85 16.60 12.60 17.10 19.10 16.10 13.35 18.40 14.70 10.60 12.60 16.20 13.60 18.90 14.10 14.50 16.15 14.75 14.80 12.45 12.65 17.35 8.60 18.40 16.10 11.60 17.75 15.25 17.65 15.60 16.35 17.65 13.60 11.70 14.35 14.75 18.25 9.90 16.00 18.25 16.85 14.60 13.85 18.95 15.60 14.85 11.75 18.45 15.90 17.10 16.10 19.90 10.95 18.45 15.10 15.00 11.35 15.95 18.10 14.60 15.40 15.40 17.60 13.35 19.10 15.35 7.60 13.40 13.90 19.10 15.25 12.90 16.10 17.35 13.15 12.15 12.60 10.35 15.40 9.60 18.20 13.60 14.85 14.75 14.10 14.90 16.25 19.25 13.60 13.60 15.65 12.75 14.60 9.85 12.65 11.90 19.20 16.60 11.20 15.25 11.90 13.20 16.35 12.40 15.85 14.35 18.15 11.15 15.65 17.75 7.65 12.35 15.60 19.30 15.20 17.10 15.60 18.40 19.05 18.55 19.10 13.10 12.85 9.50 4.50 11.85 13.60 11.70 12.40 13.35 11.40 14.90 19.90 17.75 11.20 14.60 17.60 14.05 16.10 13.35 11.85 11.95 14.75 15.15 13.20 16.85 7.85 7.70 12.60 7.85 10.95 12.35 9.95 14.90 16.65 13.40 13.95 15.70 16.85 10.95 15.35 12.20 15.10 17.75 15.20 14.60 16.65 8.10
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
1
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
0
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
0
1
2
Degree of seasonal differencing (D)
12
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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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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