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Data:
138.1 138.6 160.8 151.5 142.7 157.4 138.9 141.0 150.9 149.9 153.0 144.3 128.1 123.3 155.9 144.1 134.1 153.1 131.0 129.8 139.9 135.6 126.8 134.4 113.5 107.5 133.8 119.0 125.9 130.1 114.2 111.6 131.2 124.1 127.1 123.4 100.7 100.3 121.6 110.5 110.3 122.7 102.6 101.8 113.6 107.2 116.8 112.5 89.0 95.2 110.6 102.6 106.4 112.7 103.2 104.2 111.3 115.7 111.6 112.2 92.2 97.1 108.1 107.2 107.2 110.3 97.6 100.6 102.8 105.3 109.5 105.7 93.7 91.7 111.6 109.2 101.2 114.3 100.9 106.0 109.0 110.4 110.7 105.7 89.6 83.1 103.5 104.8 93.5 106.7 93.7 84.7 99.2 91.9 94.9 94.1 80.8 72.6 94.0 80.0 85.4 91.5 76.1 76.1 89.5 85.3 91.0 93.0 73.4 76.6 95.0 84.5 88.7 93.1 83.4 82.2 95.6 83.7 94.3 93.0 71.8 75.3 91.8 80.7 84.8 83.1 78.4 78.7 84.7 80.5 91.8 83.5 66.6 68.6 83.6 79.7 79.9 81.2 74.3 73.0 78.8 81.6 85.0 91.1 68.2 59.8 83.1 76.7 73.2 83.9 73.0 70.9
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
48
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
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 (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='lags',ylab='ACF') 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 1:par1) { 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(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-1,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(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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