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Data:
153.3 154.5 155.2 156.9 157 157.4 157.2 157.5 158 158.5 159 159.3 160 160.8 161.9 162.5 162.7 162.8 162.9 163 164 164.7 164.8 164.9 165 165.8 166.1 167.2 167.7 168.3 168.6 168.9 169.1 169.5 169.6 169.7 169.8 170.4 170.9 171.9 171.9 172 172 172.4 173 173.7 173.8 173.8 173.9 174.6 175 175.9 176 175.1 175.6 175.9 176.7 176.1 176.1 176.2 176.3 177.8 178.5 179.4 179.5 179.6 179.7 179.7 179.8 179.9 180.2 180.4 180.4 181.3 181.9 182.5 182.7 183.1 183.6 183.7 183.8 183.9 184.1 184.4 184.5 185.9 186.6 187.6 187.8 187.9 188 188.3 188.4 188.5 188.5 188.6 188.6 189.4 190 191.9 192.5 193 193.5 193.9 194.2 194.9 194.9 194.9 194.9 195.5 196 196.2 196.2 196.2 196.2 197 197.7 198 198.2 198.5 198.6 199.5 200 201.3 202.2 202.9 203.5 203.5 204 204.1 204.3 204.5 204.8 205.1 205.7 206.5 206.9 207.1 207.8 208 208.5 208.6 209 209.1 209.7 209.8 209.9 210 210.8 211.4 211.7 212 212.2 212.4 212.9 213.4 213.7 214 214.3 214.8 215 215.9 216.4 216.9 217.2 217.5 217.9 218.1 218.6 218.9 219.3 220.4 220.9 221 221.8 222 222.2 222.5 222.9 223.1 223.4 224 225.1 225.5 225.9 226.3 226.5 227 227.3 227.8 228.1 228.4 228.5 228.8 229 229.1 229.3 229.6 229.9 230 230.2 230.8 231 231.7 231.9 233 235.1 236 236.9 237.1 237.5 238.2 238.9 239.1 240 240.2 240.5 240.7 241.1 241.4 242.2 242.9 243.2 243.9
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
36
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
MA
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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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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