Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
62.4 67.4 76.1 67.4 74.5 72.6 60.5 66.1 76.5 76.8 77 71 74.8 73.7 80.5 71.8 76.9 79.9 65.9 69.5 75.1 79.6 75.2 68 72.8 71.5 78.5 76.8 75.3 76.7 69.7 67.8 77.5 82.5 75.3 70.9 76 73.7 79.7 77.8 73.3 78.3 71.9 67 82 83.7 74.8 80 74.3 76.8 89 81.9 76.8 88.9 75.8 75.5 89.1 88 85.9 89.3 82.9 81.2 90.5 86.4 81.8 91.3 73.4 76.6 91 87 89.7 90.7 86.5 86.6 98.8 84.4 91.4 95.7 78.5 81.7 94.3 98.5 95.4 91.7 92.8 90.5 102.2 91.8 95 102 88.9 89.6 97.9 108.6 100.8 95.1 101 100.9 102.5 105.4 98.4 105.3 96.5 88.1 107.9 107 92.5 95.7 85.2 85.5 94.7 86.2 88.8 93.4 83.4 82.9 96.7 96.2 92.8 92.8 90 95.4 108.3 96.3 95 109 92 92.3 107 105.5 105.4 103.9 99.2 102.2 121.5 102.3 110 105.9 91.9 100 111.7 104.9 103.3 101.8 100.8 104.2 116.5 97.9 100.7 107 96.3 96 104.5 107.4 102.4 94.9 98.8 96.8 108.2 103.8 102.3 107.2 102 92.6 105.2 113 105.6 101.6 101.7 102.7 109 105.5 103.3 108.6 98.2 90 112.4 111.9 102.1 102.4 101.7 98.7 114 105.1 98.3 110 96.5 92.2 112 111.4 107.5 103.4 103.5 107.4 117.6 110.2 104.3 115.9 98.9 101.9 113.5 109.5 110 114.2 106.9 109.2 124.2 104.7 111.9 119 102.9 106.3
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
Default
5
6
7
8
9
10
11
12
24
36
48
60
Box-Cox transformation parameter (Lambda)
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
1
2
Degree of seasonal differencing (D)
0
1
2
Seasonality
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 (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,'ACF(k)',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,'PACF(k)',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
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation