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Data:
112.7 122 134.7 109.8 130.8 118.7 104.4 87.8 134.2 143.9 140.4 111 126.3 124.4 136.1 118.4 127.4 127.9 115 90.2 131 143.3 131.5 98.5 124.9 122.4 128.8 125.9 120.2 120 116 89.2 135.9 148.7 128.1 100.9 125.5 119.8 120.7 125 109 114.2 105.6 80.1 131.1 136.6 119.7 102.4 114.5 112.9 131.8 118.7 107.1 127 104.6 85.9 134 127.6 121.5 104.5 107.3 111.9 120.7 116.9 106.1 122.3 97.8 82.7 128.2 119 127.4 106 108.7 113.5 131.4 111.3 119 130.7 104.5 88.9 135.4 140.6 138.8 107.4 120.8 124.1 139.2 119.9 121 133.7 115.2 96.7 131 147.6 132.9 97.4 123.6 124.9 118.6 127.6 110.2 115.4 106.6 75.5 116.7 118 98.7 81.5 87 86.8 96.8 92.7 82.1 94.1 89.7 67.5 102 103.2 95.6 83 87.2 94 107.7 103.3 94.8 112.7 96.8 75.9 116.7 111.4 108.6 90.9 92.6 95.7 116.7 95.4 105.1 99.7 89.8 74 108 102.1 100.2 83.2 87.9 93.3 98.5 84.5 89.3 94.2 83.5 67.5 89.4 102.4 92 65.9 85.3 87 91.8 88.5 89.1 89.8 88.9 64 93.2 100.1 89.3 68.1 94.3 93.3 98.1 96.8 87.8 95.6 95.7 64.4 108.1 109.6 90.9 75.6 93.5 98.1 104.5 102.7 89.6 108.8 95.4 70.1 104.6 105.5 96.8 79.4 92.3 96.8 103 99.5 91 103.4 82 70.1 98.1 95.7 98 77.3 89.8 91.6 106.5 87.5 99.5 104.4 84.5 68.3
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
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)
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
par8 <- '' par7 <- '0.95' par6 <- 'White Noise' par5 <- '12' par4 <- '0' par3 <- '1' par2 <- '1' par1 <- '48' 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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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