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Data:
90.2 90 88.8 85.8 84.2 80 77.8 76.8 86.4 89.2 86.2 84.6 83.2 83.2 82.6 79.8 77.2 74.8 73 73 83.6 85.6 84.8 84.2 83.4 84.6 84.6 83.8 81.2 79.6 78 78.2 88.8 92 91 91.2 90.4 91.8 92.2 90.2 88.6 87.8 86 87.2 97.6 101.2 100.4 100.2 100.2 103 104.2 104 102.4 101.8 101 102.2 114 118.4 118.8 117.2 117.2 118.4 118.8 117.2 114.4 112.6 111 110.8 120.2 124.4 123.4 121.2 119 119.8 120 118.4 115 113.4 111 111 121.6 126.2 125.8 124.8 122 123.2 124.2 120.8 116.8 114.8 111 109 119.8 124 121.6 118 115.8 116 115.8 114.4 112 110.2 107.4 108.2 117.6 121.4 119.8 115.6 112.6 113.2 112.2 110.8 108 105.2 102.4 101 110.8 116.8 113.8 108 104.4 105.2 105.4 103.2 100.6 97.8 95.8 95 104.8 110.4 106.4 102.2 98.4 98.4 98.6 96.2 92.4 91.4 88.4 87.8 97.6 104.2 100.2 97 92.8 92 93.4 92 89.6 88.6 87.2 86.2 96.8 102 102.6 100.6 94.2 94.2 95.2 95 94 92.2 91 91.2 103.4 105 104.6 103.8 101.8 102.4 103.8 103.4 102 101.8 100.2 101.4 113.8 116 115.6 113 109.4 111 112.4 112.2 111 108.8 107.4 108.6 118.8 122.2 122.6 122.2 118.8 119 118.2 117.8 116.8 114.6 113.4 113.8 124.2 125.8 125.6 122.4 119 119.4 118.6 118 116 114.8 114.6 114.6 124 125.2 124 117.6 113.2 111.4 112.2 109.8 106.4 105.2 102.2 99.8 111 113 108.4 105.4 102 102.8 103.4 101.6 98.6 98 93.8 95.6 105.6 106.8 103.6 101.2 100.4 103.2 105.6 106.6 107.2 107.4 104.8 107.2 117.4 119.4 116.2 112.8 111.6
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
60
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)
1
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
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 (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF') 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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Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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