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Data:
24.90 25.06 25.10 24.92 25.46 25.89 25.39 25.38 25.25 24.88 25.00 25.00 24.07 23.60 23.18 23.25 23.04 22.77 22.25 22.41 22.50 22.91 22.88 21.69 21.19 21.56 22.00 22.13 22.27 22.30 21.94 22.40 22.77 22.90 23.03 23.05 22.41 22.26 21.90 22.01 22.62 22.76 23.40 23.63 24.05 23.82 23.71 23.95 23.61 23.98 23.56 23.99 24.33 24.48 24.31 24.38 24.63 25.54 25.75 25.73 25.85 25.78 25.86 26.86 27.36 27.38 26.58 27.65 27.73 27.18 27.32 27.30 26.90 26.70 26.75 26.41 26.29 27.51 27.91 27.70 27.28 28.25 27.62 27.30 25.94 24.99 25.50 24.42 26.58 25.84 26.76 26.74 26.68 25.55 26.40 25.19 23.94 24.20 24.20 23.07 24.07 25.02 24.65 24.68 24.63 24.49 25.05 24.31 23.90 23.68 24.50 25.22 25.48 26.00 26.07 26.06 26.22 26.70 27.20 26.77 26.11 25.43 24.99 25.51 24.00 23.86 22.96 23.41 23.17 24.12 23.87 24.27 24.40 24.16 25.15 25.09 24.60 24.33 24.14 24.36 25.40 26.15 26.77 26.94 26.33 26.24 26.23 25.88 27.00 26.91 27.15 27.78 28.73 28.83 28.68 27.56 27.15 27.41 27.47 28.76 28.47 27.94 27.23 27.01 26.15 26.11 27.20 27.36 27.33 27.43 28.92 29.45 29.01 29.25 29.14 29.64 30.40 30.62 31.25 31.75 31.30 30.70 31.03 31.46 31.28 31.03 30.95 31.17 31.29 31.91 32.10 31.71 31.90 32.02 32.65 33.77 33.51 34.26 34.21 34.13 34.73 34.73 34.57 34.80 33.98 34.40 34.21 34.61 35.25 35.23 35.00 34.52 33.82 34.35 34.81 34.96 36.69 36.42 36.44 37.41 36.40 36.15 35.78 36.95 36.14 36.36 37.31 37.58 38.00 37.23 37.00 37.87 37.70 36.17 36.56 37.70 38.77 39.02 39.88 39.56 38.52 37.20 38.58 39.41 39.08 38.81 38.73 38.70 39.23 39.82 39.97 40.37 39.54 39.21 39.07 39.78 39.40 38.92
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
Valutakoersen Eur-Dollar
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)
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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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