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Data:
50 52.4 57.5 52.5 57.5 57.6 48.3 52 62.1 59.1 62.6 57.9 59.3 61.5 66 61.1 63.8 69.6 57 59.9 63.8 69.8 64.6 60.8 64.7 63.6 68.8 66.4 64.4 65.3 63 61.1 67.7 72.3 65.4 63.2 69.4 62.3 71 68.6 62 68.2 66.8 65.5 76.9 78.1 67.6 80.1 64.7 70.4 84.6 75.1 69.6 81.8 74.2 72.9 84.9 80.5 79.6 90.8 76.5 70.9 82.3 77.8 75.6 81.3 71 75.1 89.2 84.1 82.7 82.4 78.2 78.5 91.5 76.6 80.6 85.9 74.5 79.4 89.7 92.7 89.6 87 80.9 76.2 89.7 79.1 82.4 90.3 85.8 83.5 85.1 90.6 87.7 86 89.7 86.2 91.1 91.3 85.5 92 91.5 80 100.9 97.3 89.1 104 80.2 83.3 97.5 86.8 84.3 93.4 90.2 82.5 93.7 93.9 91.1 96.9 88.2 100.9 109.5 91 89.5 109.6 97.9 94.9 103.5 100 107.1 108 95 102.2 131.4 104.5 105.6 106.1 98 113 113.2 105.4 100.1 100.7 96.1 98.2 123.5 93.9 94.8 103.5 105.3 105.8 112 114.5 108.3 103.8 103 97.7 118.7 115.1 110 117.3 119.1 105.9 114.1 124.6 117.3 115 103.6 113.4 122 122.5 119.6 132.6 113 107.5 139.3 134.6 125.6 124 111.9 101.5 130.2 121.9 111.3 122 116.4 119.1 133 128.9 126.1 122.3 110.2 113.6 131 123.2 120.7 142.8 131.7 131.6 139 128.5 122.7 148.4 118.6 126.3 141 120.9 127 138.5 131.9 136.3
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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0 seconds
R Server
Big Analytics Cloud Computing Center
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