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Data:
100.21 100.36 100.62 100.78 100.93 100.70 100.00 100.20 99.68 99.56 100.06 100.50 99.30 99.37 99.20 98.11 97.60 97.76 98.06 98.25 98.50 97.39 98.09 97.78 98.12 97.50 97.30 97.64 96.88 97.40 98.27 97.94 98.61 98.72 98.62 98.56 98.06 97.40 97.76 97.05 97.85 97.40 97.27 97.93 98.60 98.70 98.88 98.27 97.85 97.70 96.97 97.72 97.66 99.00 98.86 99.56 100.19 100.37 100.01 99.68 99.78 99.36 99.21 99.26 99.26 100.43 101.50 102.27 102.69 103.47 104.02 103.55 103.77 104.19 103.64 103.68 105.39 106.61 108.12 109.22 110.17 110.31 111.06 111.14 111.39 112.51 111.28 112.22 113.19 114.32 115.34 116.61 117.83 117.70 118.51 118.82 119.49 119.57 120.00 121.96 121.45 123.41 124.44 126.25 127.41 127.63 129.19 129.82 130.45 132.02 132.72 132.96 135.06 137.04 137.83 139.17 140.35 141.01 141.89 143.28 142.90 143.37 145.03 146.05 147.39 149.58 151.02 153.57 155.60 157.18 158.77 159.95 161.34 161.95 163.36 165.00 166.65 168.65 170.29 172.70 173.79 176.45 177.58 179.19 181.01 184.08 185.63 188.51 190.18 192.19 193.47 196.73 200.39 203.24 205.53 208.21 208.88 212.85 216.41 216.23 219.27 222.02 224.89 230.37 232.29 235.53 236.92 242.37 242.75 244.19 247.94 248.80 250.18 251.55 254.40 255.72 257.69 258.37 258.22 258.59 257.45 257.45 256.73 258.82 257.99 262.85 262.58 261.55 261.25 259.78 256.26 254.29 248.50 241.88 238.53 232.24 232.46 225.79 221.63 219.62 215.94 211.81 205.57 201.25 194.70 187.94 185.61 181.15 186.50 183.21 182.61 187.09 189.10 191.25 190.74 190.79
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
Default
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
-1.0
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
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 (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma' par7 <- as.numeric(par7) 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='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')) 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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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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