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Data:
78 100.1 113.2 93.1 115.4 103.3 45.1 104.7 111.3 111.5 100.9 82.1 85.4 97.7 106.6 92.6 109.2 110 52.5 105.3 102.3 118.5 100 74.4 89.2 91.9 107 103.6 101.8 105.1 55.5 92.1 109.8 112.7 98.5 70.3 84.5 91.1 107.6 102.2 96 107.3 59.9 90.2 116.3 115.6 92 76.5 87.9 95.8 116.9 102.9 95.8 117.3 52.8 100.1 116.3 111.8 98.5 86.2 79.9 92.3 100.5 112.5 101.1 121.5 49.6 104.8 120.4 108.3 105.2 85.7 86.8 95.1 117 100.1 112.3 119.6 51.8 105.5 119.9 115.4 112.8 85.1 96.2 103.6 119.9 103.7 109 119.6 57 109.2 112.6 126 109.7 80.1 105.8 114.1 98.3 125.3 111.6 119.7 65 99 124.5 119 98.8 81.8 90.3 102 119.3 104.3 102.8 118.8 60.9 101 122.6 122.2 95 75.6 83.1 89.8 126.1 108.6 98.9 124.3 56.8 102.7 121.7 118.2 101 69 88.6 109.6 128.2 102 122.7 110.5 54 108.1 125 114.1 112.4 87.3 95.4 96.9 125.8 102 112.5 118.9 62.7 110 114.7 124.4 111.9 77 84.1 96.5 106.8 107.9 107.5 114.3 66.6 97.9 117.8 123.8 103.3 84.2 103.6 103.6 112.2 102.7 100.8 109.4 63.5 92.3 119.2 121.5 97.6 78.3 95.6 97.9 114.4 100.9 94.4 117.2 61 95.8 116.2 118.5 94.3 74.4 94.9 102 102.9 109.5 99.7 118.3 56.2 100.3 116.9 108.7 93.9 85.3 85.3 102.4 121.6 91.4 110.2 112.7 55.7 100.1
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
12
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)
1
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
par8 <- '' par7 <- '0.95' par6 <- 'White Noise' par5 <- '12' par4 <- '1' par3 <- '0' par2 <- '1' par1 <- '12' 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')
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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