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Data:
10.31 11.17 12.42 13.24 14.19 14.04 14.47 15.5 15.73 16.39 15.54 14.76 14.89 14.37 15.33 15.75 15.68 16.24 17.27 17.95 18.26 18.74 18.73 19.53 19.87 19.95 20.24 21.42 22.34 22.33 22.87 22.18 21.23 21.58 21.6 21.54 23.19 24.01 24.51 24.16 24.65 24.51 24.32 23.19 23.45 25.15 25.54 25.55 26.04 26.49 25.94 26.31 28.39 26.97 25.28 24.73 23.75 22.1 22.79 23.63 24.54 26.42 27.64 27.67 27.9 28.07 29.34 28.49 29.11 28.52 28.01 27.24 25.22 25 26.47 27.67 28.4 31.66 30.47 31.67 28.96 28.75 30.54 30.03 30.66 29.51 30.11 30.98 30.28 29.5 25.24 23.01 21.82 20.89 21.87 23.01 23.37 24.63 24.33 26.92 25.79 24.78 23.81 24.65 23.69 23.17 23.38 23.48 24.63 25.44 25.02 25.88 25.79 25.89 26.87 27.1 26.8 26.48 24.92 24.34 23.62 23.8 22.83 23.42 23.71 24.47 24.93 24.25 24.89 25.02 25.02 25.31 21.47 20.39 19.92 19.29 19.21 18.62 17.87 17.68 16.51 17.29 17.82 16.91 17.58 18.57 18.94 19.71 18 17.56 18.18 18.51 19.19 18.74 19.11 20.4 21.92 22.88 23.38 25.09 23.88 22.84 24.18 24.66 24.13 24.89 23.8 22.99 22.47 22.08 23.22 23.68 23.65 24.01 24.86 24.2 24.11 23.91 24.86 26 25.83 25.85 26.92 26.75 27.55 27.5 26.89 26.93 25.69 24.28 23.02 21.94 22.5 23.32 24.15 24.84 27 28.5 28.47 28.22 29.05 29.35 29.18 29.61 30.86 30.92 31.43 31.44 31.33 26.92 24.21 24.94 23.24 23.23 23.55 21.65 21.85 23.51 24.04 24.2 25.04 25.51 24.58 25.04 26.13 26.47 27.25 26.64 26.54 28.18 28.23 27.62 28.26 27.15 25.98 24.98 24.88 26.37 27.75 29.48 27.78 27.14 26.61 27.81 28.49 27.6 27.35 28.21 28.97 28.32 28.22 29.2 29.97 29.52 28.84 27.43 27.45 28.8 29.21 30.54 30.92 31.26 31.28 30.02 30.14 31.33 31.42 31.91 33.72 34.99 35.98 35.28 35.09 33.08 32.9 33.05 32.02 34.36 34.99 35.74 36.6 37.64 38.58 40.49 39.67 37.63 37.58 37.78 39.85 41.19 42.2 44.03 44.08 44.72 40.99 38.7 36.18 38.26 37.23 32.75 33.76 36.3 33.29 35.21 38.3 39.7 41.89 40.55 39.59 41.4 44.04 45.15 47.23 48.69 49.6 48.06 50.95 47.2 46.93 48.58 45.8 45.64 43.53 44.25 47.29 49.05 50.16 53.08 53.52 54.35 54.49 52.62 53.59 55.51 58.07 59.61 59.95 62.36 60.22 58.1 59.63 58.83 56.35 54.62 54.04 53.45 53.62 52.73 50.24 50.29 50.28 52.94 54.76 52.64 52.22 56.18 57.89 59.34 60.9 60.65 58.26 55.78 55.76 57.51 57.54 57.75 57.89 59.91 62.17 63.68 66.99 67.59 67.53 66.03 64.63 64.12 65.11 64.81 63.33 63 65.7 68.32 69.12 70.26 68.71 70.23 72.1 68.67 67.48 65.87 63.38 59.77 57.55 56.07 55.73 55.22 55.63 55.45 55 55.8 56 54.73 57.27 59.41 58.1 58.46 57.33 55.06 50.75 48.39 50.17 51.81 54.19 53.74 54.4 57.98 58.3 57.94 57.73 61.7 64.73 64.52 63.99 64.35 64.42 62.55 64.9 67.39 65.88 66.99 66.63 68.51 68.02 70.15 73.73 74.5 73.74 73.83 69.98 68.5 67.52 68.83 71.76 73.84 75.54 76.52 76.18 76.22 80.58 81.62 86.47 86.47
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
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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