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Data:
9.1 9.27 9.59 10.64 12.17 12.81 12.33 11.92 11.92 12.17 12.33 10.39 10.96 11.44 11.36 11.84 11.2 12.17 11.92 11.92 12.73 12.89 15.47 17 14.91 13.62 12.89 12.33 12.33 11.36 10.96 11.36 10.15 9.35 9.59 9.59 9.67 9.19 9.02 8.94 8.38 8.3 8.14 8.3 8.54 9.02 9.27 9.02 9.02 8.38 8.46 7.9 7.17 7.25 7.33 7.41 7.98 7.65 7.41 7.57 7.41 7.49 7.49 8.14 8.38 8.22 8.46 7.98 8.06 8.06 8.54 9.75 12.17 15.23 15.79 15.39 14.34 13.78 13.21 12.65 11.84 11.84 11.6 11.04 10.64 10.39 10.15 9.67 9.67 9.91 9.91 9.91 9.71 9.51 9.32 9.12 9.22 9.22 8.92 8.82 8.82 8.82 8.72 8.34 8.14 8.14 8.04 8.04 8.04 8.14 8.24 8.34 8.53 8.63 8.53 8.72 9.11 8.92 8.82 9.21 9.21 9.4 9.6 9.69 9.74 10.64 12.82 15.06 17.3 20.04 17.9 16.77 17.07 17.1 17.53 17.7 17.37 17.13 17.13 16.7 15.23 13.66 12.96 13.39 13.73 13.86 14.36 14.09 13.89 14.03 14.73 16.3 17.3 17.6 18 19.54 22.34 24.08 23.85 24.08 25.98 26.55 26.75 26.88 26.78 27.18 28.15 28.92 29.16 29.62 29.92 30.26 30.62 31.03 31.56 32.46 33.4 34.8 36.67 38.84 40.51 41.85 44.45 49.33 53.84 56.94 60.61 65.22 72.57 82.38 90.93 96.5 99.6 103.9 107.6 109.6 113.6 118.3 124 130.7 136.2 140.3 144.5 148.2 152.4 156.9 160.5 163 166.6 172.2 177.1 179.9 184 188.9 195.3 201.6 207.34 215.3 214.54
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)
1
0
1
2
Seasonality
1
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')
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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