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Data:
NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 76.83 77.74 80.47 79.56 82.28 100.92 113.2 90.92 86.83 82.74 83.65 80.92 83.19 83.65 83.65 83.65 86.83 100.47 91.38 101.38 95.92 88.19 88.19 80.47 80.92 79.56 80.92 88.19 91.83 96.38 97.29 102.29 99.1 92.74 87.29 85.47 91.38 92.74 89.56 88.65 93.2 99.56 109.11 124.56 115.47 96.38 92.29 86.83 87.29 85.92 85.92 88.65 91.83 112.29 101.83 125.02 102.74 95.01 91.83 86.38 87.29 88.19 89.1 89.1 103.65 127.75 125.47 125.47 109.11 100.01 95.01 85.01 86.83 86.83 86.83 86.83 100.47 111.38 105.47 102.74 105.01 96.38 94.1 86.83 92.74 93.2 95.47 96.38 99.56 120.47 123.2 114.11 120.93 102.74 101.83 95.47 100.01 100.01 98.2 100.01 103.65 114.56 134.11 131.84 113.65 107.29 102.29 94.56 97.29 98.2 95.47 100.47 116.38 117.29 140.93 120.02 111.38 108.65 105.92 99.1 101.83 102.74 102.74 105.47 108.65 139.57 110.47 118.65 120.02 109.11 108.2 101.38 106.38 108.65 107.74 105.92 129.56 139.11 125.93 123.65 118.65 110.47 110.02 100.47 104.1 106.6 105.5 107.5 117.9 136.3 156.8 135.8 130 117.5 115.8 105.5 111.6 113.2 113.1 112.5 120 147.6 149.9 131.2 134.6 122.2 117.7 106.8 111.5 111.3 109.5 112.1 127 135.9 150.4 135.6 134.9 124.1 120.8 112.8 117.4 118.6 119.2 119.7 128.6 142.8 170 145.9 140.1 128.7 123.4 114.6 120.2 122 121.3 123.2 141.1 129.7 152.4 141.9 137 129 124.6 117.3 122.7 121 122 122 126.3 158.1 164.9 143.3 151.4 136.8 133.1 124.8 132.6 130.2 129.6 129.7 133.7 148.3 155.1 157.2 147.2 142.7 135.9 123.8 132.3 132.7 130.7 129.9 145.5 156.6 161.7 156 146.1 136.8 132.5 129.5 129.5 134.7 136.6 138.4 149.6 159.5 171.4 162.1 163.1 152.4 145.5 133.9 136.6 139.4 141.2 144.9 181.4 187 211.4 178.1 168 154.4 150.4 139.4 144.7 143 148.3 152.7 173.3 226.3 218.2 184.6 174.9 161.4 161.4 145.8
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) 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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0 seconds
R Server
Big Analytics Cloud Computing Center
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