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Data:
52 54.9 60.5 54.8 60.1 60.3 49.8 53.8 64.8 62 65.2 60.1 61.2 63.6 68.6 63.1 66.5 71.9 58.1 61.5 66.2 72.3 67 62.9 66.4 65.6 70.9 68.4 66.4 67.6 64.1 62.1 70 74.4 67 64.8 70.7 64 72.5 70.4 63.6 69.8 67.7 66.4 78.9 79.9 69.1 81.2 66 71.8 86.1 76.1 70.5 83.3 74.8 73.4 86.5 82 80.8 91.5 77 72.3 83.5 79 76.7 83.1 71.1 75.5 90.9 85.4 84.8 83.8 79.3 79.9 93 78.1 82.3 87.3 74.6 80 91.3 94.2 90.9 88 81.6 77.4 91 79.9 83.4 91.6 85.2 84.1 87 92.8 89.2 87.3 89.5 86.8 92 92.2 86.4 92.9 91.2 80.3 102 99 89.2 103 80.4 83.4 97.6 87 84.4 94.1 88.9 82.3 94.7 94.5 91.6 96.8 87.9 99.9 109.5 91.2 89.4 109.7 96.9 94.1 104.4 100.8 107.4 108.9 95.2 102.7 130.9 104 106.5 106.1 97.8 112.2 114.5 105.8 101 101.2 96.5 99.5 123.8 94.6 95.8 105.4 104.4 105.2 112.7 114.8 108.9 103.8 102.5 98.1 118.2 114.8 109.9 116.7 116.9 104.4 113.5 123.8 116.4 114.1 102.8 112.7 121.1 120.8 117.8 130.4 110.9 105.4 137.6 133.3 123.3 122.8 110.2 101.4 128.7 120.6 110.1 121.6 113 115.9 131.1 127.4 123.9 120.8 108.5 112.9 129.6 121.3 119.1 140.8 127.4 128.1 136.6 126.5 120.8 144.3 116 123.4 138.6 118.3 124.2 136 127.4 131.6
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
880.01two.sidedtwo.sidedtwo.sided1212Default84848DefaultDefault48
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
00grey0.990.950.950.951011Do not include Seasonal DummiesInclude Seasonal Dummies111
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)
FALSE0.01202020000Linear TrendLinear Trend000
0
1
2
Degree of seasonal differencing (D)
Unknown00000000
0
1
2
Seasonality
12121200121212
12
1
2
3
4
6
12
CI type
White NoiseWhite NoiseWhite Noise1212White NoiseWhite NoiseWhite 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 <- '0' par3 <- '0' par2 <- '1' par1 <- 'Default' 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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