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Data:
-45.6 16.1 23.9 39.3 -39.4 -0.3 17.3 17.7 31.4 -28.6 -17.2 -79 -47.9 9.1 10.6 -23.9 -45 -42.2 43.2 32.1 -15.3 21.8 -12 -95.8 -14.3 47.8 64.8 40.2 -28.8 23.5 70.3 12.3 43.5 -30.1 -5.3 -24 11.1 21.5 38.5 16.8 -36.2 6 26.6 -8 13.2 -23.6 19.4 -46.2 -8.2 33.8 16.6 5.4 -25 -5.3 16.7 19 24.8 -11.4 4.9 -58.7 16.8 13.6 6.4 22.8 -19.6 2.2 19.8 -10.7 4.7 -44.5 -34.7 -119.7 -42.2 -5.4 19.1 18.8 -2.3 0.2 20.9 3.7 50.4 -18.6 10.6 -66 10 27.2 13.5 47.2 -20.3 23.1 12.6 19.8 5.4 -25.2 -6.5 -46.5 -2.6 -0.3 38.5 -8.9 -38 19.5 51.7 19.4 18.2 -50.8 -6.1 -54.6 12.1 26.3 19.5 -0.8 -49.6 28.8 31.7 2.3 3.8 -66.2 -20.5 -113.2 -65.2 -3.9 9.1 23.2 -39.1 12.5 49.1 54.9 30.8 -3.5 -28.3 -61 -2 40 74 23.1 -45.3 17.5 25.8 15.2 -3.6 -40.5 11.5 -59.8 23.3 -27.8 55.7 22.7 -79.2 28.8 17.3 39.6 -22.2 -43 -50.3 -86.5 -31.9 23.1 53.6 21.6 -64.2 35.2 52.1 40.6 17.1 -7.8 -10 -58 14 15.8 46 -8.9 -26.7 39 -1.3 38.7 22.1 -49.2 -3.4 -86.7 -24.3 42.8 44.9 4.4 -60.5 41.4 38.5 28.5 7.6 -46.4 7 -73 5.7 23.6 39.4 30.3 -92.5 77.8 12.4 28.9 6.4 -12 -9.1 -53.2 -23.1 47.3 20.7 27.8 -84.3 62.8 26.4 32.3 13.3 -17.9 10 -45.6 13.5 11.9 26 -6.3 -79.9 54.2 22.9 31.8 3.8 -11.4 -8.6 -49.4 -2.5 23 29 20.6 -117 37.9 30.7 4.7 -5.7 4.9 18.3 -35.4 -21.3 35.8 43.8 18.7 -131.1 39.8 44.5 16.5 9.7 -6.6 15.8 -45.7 -4.8 17.6 20.5 24.2 -109 20.8 31.2 -8.8 11.8 13 8.3 -77.9 -38.8 6.1 18.1 16.8 -128.5 15.9 29 -7.2 3.3 -34.8 -2.9 -77.8 -2.8 26.7 48.1 30 -109.6 16 26.9 22.1 27 -24.5 12 -75.2 3.5 19.7 51.8 35.3 -108.2 25.3 31.6 19.9 18.8 20.4 15 -55.9 -17 33.3 33.8 37.5 -104.8 29.7 34.2 4.3 40.2 -29.3 -0.2 -95 -13.2 38.5 45.4 15.7 -123.6 12 37.5 -31.7 15.8 -64.1 -42.1 -207.4 -12.9 -5 53.9 19.7 -94.6 36 51.3 17.4 27.8 1.3 3.6 -97.9 14.1 50.8 63.5 58.6 -135.1 7.8 25.5 29.6 19.3 -26.2 7.3 -82.6 -26.1 55.3 98.8 41.7 -130.2 51.2 18.4 32 21.6 -12.5 46.6 -101.7 15.8 26 79.1 23.1 -86.9 -11.2 50.7 13.4 33.7 -16.9 -9.6
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
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 (par8 != '') par8 <- as.numeric(par8) 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
Summary of computational transaction
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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