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Data:
58.5 59.8 64.6 62.2 68 64.3 58.9 64.8 67.5 76.2 73.7 70.4 67.7 63.7 72.4 66 70.1 70.4 66.6 72.6 74 79 76.1 72.3 71.6 67.2 73.8 70.8 71.4 70.4 70.7 70.6 75.5 82.1 74.3 76.3 74.5 71.1 73.3 73.8 69 71.1 71.9 69 77.3 82.8 74 77.6 72.3 70.7 81 76.4 72.3 79.5 73.3 74.5 82.7 83.8 81.6 85.5 76.7 71.8 80.2 76.8 76.1 80.7 71.3 80.9 85 84.5 87.7 87.7 80.2 74.4 85.8 77 84.5 83.6 77.7 85.7 87.9 93.7 92.3 87 89.1 81.3 92.7 83.9 87.3 89.1 86.9 91.7 93 105.3 101.6 94.2 100.5 95.8 95.8 102.1 96 96.8 98.9 93.4 105.5 110.9 98.6 102.6 93.5 90.8 99.7 97.8 91.1 98.1 96 93.5 101.2 105.2 98.9 101.3 92.1 90.6 105.4 98.4 92.7 101.2 93.4 98.3 104.3 107 107.7 108.9 99.6 96.1 109 99.5 104.6 99.9 94.1 105.3 110.4 110.5 110 108.5 104.3 101.2 109.2 99.6 105.6 106.2 102.2 107.5 105.8 120.5 113.2 104.3 107.7 99.2 105.1 104.3 106.1 100.8 106.7 101.6 104.4 114.8 105.4 104 102 96.5 102.3 105.3 101.9 102.2 102.8 100.4 110.7 116.4 106 109.2 103 99.8 109.8 107.3 101.2 111.8 106.9 103.5 113.1 119.4 113.3 115 104.7 107.2 116.6 111.3 111.4 115 102.4 111.4 113.2 112.9 114.2 115.6 107.1 102.3 117.9 105.8 114.3 113.1 102.9 112.2
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
1Full Box-Cox transformDefault1Default112DefaultDefault111111111111111111Default111Default
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
1-211111111111111110101111111101
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)
1211111011001101101000010010000
0
1
2
Degree of seasonal differencing (D)
10112012010011001001121212121212121201212120
0
1
2
Seasonality
No12121212121212121212121212121212
12
1
2
3
4
6
12
CI type
White NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite NoiseWhite 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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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