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Data:
181896.00 181580.00 181234.00 180598.00 187123.00 186807.00 181896.00 178638.00 178954.00 178954.00 179269.00 179936.00 181580.00 179616.00 181580.00 179936.00 185158.00 187469.00 177656.00 175025.00 177305.00 176989.00 175025.00 175345.00 179269.00 178638.00 179269.00 179269.00 183545.00 184176.00 172398.00 172398.00 176989.00 174709.00 170785.00 172398.00 176327.00 174363.00 174047.00 169803.00 176007.00 177305.00 164545.00 164229.00 170785.00 167176.00 160967.00 163598.00 166509.00 167176.00 165212.00 161287.00 169456.00 169456.00 155078.00 154101.00 158025.00 150838.00 143616.00 145932.00 150838.00 146910.00 144283.00 138710.00 146247.00 146563.00 132190.00 131839.00 134470.00 126301.00 117465.00 121043.00 125950.00 120728.00 120412.00 115154.00 123670.00 125319.00 109266.00 105688.00 107968.00 99132.00 89981.00 92928.00 98470.00 91946.00 92928.00 89004.00 97172.00 98150.00 78524.00 77221.00 80799.00 71333.00 62817.00 65764.00 72950.00 64462.00 63799.00 57244.00 64462.00 66742.00 46448.00 46448.00 49391.00 41542.00 32706.00 37297.00 45466.00 36631.00 40244.00 35333.00 43186.00 45813.00 24853.00 23240.00 26502.00 18649.00 12444.00 15040.00
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)
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
White Noise
MA
Confidence Interval
Use logarithms with this base
(overrules the Box-Cox lambda parameter)
(?)
Chart options
R Code
par8 <- '' par7 <- '0.95' par6 <- 'White Noise' par5 <- '12' par4 <- '0' par3 <- '0' par2 <- '1' par1 <- '60' 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 (par8 != '') par8 <- as.numeric(par8) x <- na.omit(x) ox <- x if (par8 == '') { if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } } else { x <- log(x,base=par8) } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='picts.png') op <- par(mfrow=c(2,1)) plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value') if (par8=='') { mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } else { mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } plot(x,type='l', main=mytitle,xlab='time',ylab='value') par(op) dev.off() bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub) 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')
Compute
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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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