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Data:
63 59 10 21 12 63 61 59 70 26 74 82 40 39 28 92 2 54 25 40 96 4 29 22 14 47 15 80 92 22 57 42 53 72 15 65 29 7 84 1 36 56 86 65 75 85 12 11 68 79 90 71 68 23 6 97 6 65 18 78 14 98 79 30 74 1 36 25 41 93 39 77 51 67 51 85 71 71 74 45 47 83 82 23 73 67 14 84 40 67 52 23 4 56 81 75 37 30 56 51 67 50 67 59 17 53 44 6 49 84 52 37 4 53 52 43 94 50 39 61 96 2 38 11 82 40 46 99 81 39 88 66 24 27 23 54 85 66 48 18 57 78 90 15 72 5 25 97 37 39 22 88 39 30 79 7 91 36 14 91 34 10 71 28 40 92 47 34 60 79 23 26 29 3 97 52 48 15 62 96 12 31 72 45 75 54 45 64 9 41 85 11 59 11 21 95 45 88 47 18 100 46 27 2 79 69 33 31 34 31 63 94 26 92 80 70 88 57 54 70 80 17 53 86 95 58 8 28 48 2 69 2 52 57 60 22 90 35 87 42 29 6 3 88 17 1 62 3 26 12 74 39 2 8 66 43 86 33 63 36 69 73 8 74 64 6 98 93 60 93 55 4 42 79 2 36 8 79 2 80 79 37 28 54 21 72 84 11 34 70 12 9 76 60 7 43 70 54 13 55 95 49 69 30 11 66 19 47 44 32 51 57 2 39 83 70 10 82 67 24 0 9 58 62 77 56 36 16 38 41 18 46 4 12 16 82 48 58 79 94 62 62 99 14 85 87 61 27 96 10 24 40 16 59 18 79 91 38 75 4 85 4 19 40 1 10 61 33 53 46 86 83 56 72 66 60 36 7 87 42 98 53 85 18 70 22 12 35 94 5 56 42 0 58 18 66 67 68 76 59
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')
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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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