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Data:
77.7 78.89 90.2 77.26 80.76 84.93 66.08 71.56 80.78 83.31 85.3 73.94 78.7 81.32 86.8 80.76 84.46 84.21 73.64 70.85 83.78 89.12 78.93 80.54 81.67 82.53 88.2 89.17 83.7 89.79 77.58 70.11 88.07 92.49 83.33 90.05 82.91 88.52 96.42 90.87 86.4 97.47 85.67 79.91 95.73 94.6 91.92 90.38 82.31 87.82 101.29 89.58 87.83 99.95 82.67 84.65 97.83 97.47 97.66 99.14 90.02 100.97 112.48 91.44 108.46 98.41 89.35 92.8 100.43 104.85 108.36 101.54 105.26 101.8 112.36 99.5 104.65 101.13 89.8 87.84 96.41 103.26 100.31 92.33 96.19 96.37 103.06 101.5 101.88 100.85 95.56 87.6 101.18 110.8 101.1 104.42 103.27 100.87 107.8 104.99 100.76 104.46 100.62 87.84 107.31 115.61 103.43 109.93 104.43 106.69 123.1 109.42 101.46 124.48 101.49 100.46 115.51 113.37 115.4 118.2 106.82 110.17 119.91 112.31 110.62 120.37 97.94 103.02 116.36 108.51 122.54 121.32 112.25 109.89 129.58 107.2 118.68 118.25 102.67 104.19 117.74 123.3 122.2 112.71 118.53 115.32 127.36 110.45 122.22 123.39 116.2 109.22 116.98 132.89 125.24 115.68
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)
0
0
1
2
Degree of seasonal differencing (D)
0
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 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')
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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