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Data:
46.8 52.8 58.3 54.5 64.7 58.3 57.5 56.7 56 66.2 58.2 53.9 53.1 54.4 59.2 57.8 61.5 60.1 60.1 58.4 56.8 63.8 53.9 63.1 55.7 54.9 64.6 60.2 63.9 69.9 58.5 52 66.7 72 68.4 70.8 56.5 62.6 66.5 69.2 63.7 73.6 64.1 53.8 72.2 80.2 69.1 72 66.3 72.5 88.9 88.6 73.7 86 70 71.6 90.5 85.7 84.8 81.1 70.8 65.7 86.2 76.1 79.8 85.2 75.8 69.4 85 75 77.7 68.5 68.4 65 73.2 67.9 76.5 85.5 71.7 57.9 75.5 78.2 75.7 67.1 74.6 66.2 74.9 69.5 76.1 82.3 82.1 60.5 71.2 81.4 74.5 61.4 83.8 85.4 91.6 91.9 86.3 96.8 81 70.8 98.8 94.5 84.5 92.8 81.2 75.7 86.7 87.5 87.8 103.1 96.4 77.1 106.5 95.7 95.3 86.6 89.6 81.9 98.4 92.9 83.9 121.8 103.9 87.5 118.9 109 112.2 100.1 111.3 102.7 122.6 124.8 120.3 118.3 108.7 100.7 124 103.1 115 112.7 101.7 111.5 114.4 112.5 107.2 136.7 107.8 94.6 110.7 126.6 127.9 109.2 87.1 90.8 94.5 103.3 103.2 105.4 103.9 79.8 105.6 113 87.7 110 90.3 108.9 105.1 113 100.4 110.1 114.7 88.6 117.2 127.7 107.8 102.8 100.2 108.4 114.2 94.4 92.2 115.3 102 86.3 112 112.5 109.5 105.9 115.3 126.2 112.2 112.5 106.9 90.6 75.6 78.8 101.8 93.9 100 89.2 97.7 121.1 108.8 92.9 113.6 112.6 98.8 78
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)
0
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) x <- na.omit(x) 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,'ACF(k)',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,'PACF(k)',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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R Server
Big Analytics Cloud Computing Center
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