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Data:
122.36 123.33 123.04 124.53 125.13 125.85 126.50 126.53 127.07 124.55 124.90 124.32 122.84 123.31 123.31 124.87 124.64 124.73 124.90 124.04 123.28 123.86 122.29 124.09 124.54 125.65 125.70 125.53 125.61 125.55 125.41 127.60 124.68 124.41 126.43 126.38 125.78 124.70 125.07 125.25 126.58 127.13 125.82 123.70 124.39 123.70 124.42 121.05 121.02 123.23 121.32 120.91 120.72 123.31 119.58 119.53 120.59 118.63 118.47 111.81 114.71 117.34 115.77 118.38 117.84 118.83 120.02 116.21 117.08 120.20 119.83 118.92 118.03 117.71 119.55 116.13 115.97 115.99 114.96 116.46 116.55 113.05 117.44 118.84 117.06 117.54 119.31 118.72 121.55 122.61 121.53 123.31 124.07 123.59 122.97 123.22 123.04 122.96 122.81 122.81 122.62 120.82 119.41 121.56 121.59 118.50 118.77 118.86 117.60 119.90 121.83 121.84 122.12 122.12 121.36 119.66 119.32 120.36 117.06 117.48 115.60 113.86 116.92 117.75 117.75 115.31 116.28 115.22 115.65 115.11 118.67 118.04 116.50 119.78 119.95 120.37 119.79 119.43 121.06 121.74 121.09 122.97 120.50 117.18 115.03 113.36 112.59 111.65 111.98 114.87 114.67 114.09 114.77 117.05 117.22 113.18 110.95 112.14 112.72 110.01 110.29 110.74 110.32 105.89 108.97 109.34 106.57 99.49 101.81 104.29 109.73 105.06 107.97 108.13 109.86 108.95 111.20 110.69 106.10 105.68 104.12 104.71 104.30 103.52 107.76 107.80 107.30 108.64 105.03 108.30 107.21 109.27 109.50 111.68 111.80 111.75 106.68 106.37 105.76 109.01 109.01 109.01 109.01 107.69 105.19 105.48 102.22 100.54 105.00 105.44 107.89 108.64 106.70 109.10 105.23 108.41 108.80 110.39 110.22 110.86 108.58 107.70 106.62 109.84 107.16 107.26 108.70 109.85 109.41 112.36 111.03 110.67 109.21 113.58 113.88 114.08 112.33 113.92 114.41 114.57 115.35 113.13 113.29 112.56 113.06 113.46 115.39 116.62 117.04 117.42 115.62 115.16 115.69 112.85 114.05 112.00 113.74 116.26 118.63 116.49 118.23 116.83 118.82 114.36 112.02 113.24 109.75 110.33 112.86 113.04 113.80 110.90 109.96 108.69 108.84 108.47 108.07 107.94 108.11 108.11 106.81 105.58 105.61 106.52 103.86 104.60 104.73 105.12 104.76 103.85 103.83 103.22 101.64 102.13 104.33 104.92 107.78 104.49 102.80 102.86 104.51 104.73 102.58 99.93 101.41 101.05 99.86 101.11 100.89 101.09 98.31 98.08 99.55 99.62 97.37 98.16 97.98 98.15 97.10 97.24 96.70 96.64 100.65 96.75 97.74 97.92 98.34 93.84 97.80 96.20 95.99 95.18 95.95 92.23 91.78 92.97 89.76 92.88 96.23 95.79 93.97 93.90 93.60 93.96 88.69 88.57 85.62 86.25 85.33 83.33 77.78 78.70 72.05 80.75 81.41 82.65 75.85 75.70 78.25 77.41 76.84 74.25 74.95 68.78 73.21 73.26 78.67 75.63 74.99 83.87 79.62 80.13 79.76 78.20 78.05 79.05 73.32 75.17 73.26 73.72 73.57 70.60 71.25 74.22 73.32 73.01 74.21 75.32 71.73 71.94 72.94 72.47 71.94 74.30 74.30
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)
0
0
1
2
Seasonality
1
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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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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