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Data:
-35.68 -39.41 -90.98 -99.79 9.899 -55.35 -82.98 -26.85 -130.7 -196 103.4 -17.23 29.32 217.6 119 73.21 -47.1 -46.35 163 143.1 9.336 159 193.4 312.8 307.3 107.6 95.02 138.2 182.9 179.6 153 269.1 -90.66 143 184.4 26.77 357.3 220.6 237 84.21 353.9 286.6 323 32.15 68.34 127 348.4 488.8 374.3 415.6 79.02 456.2 380.9 246.6 370 255.1 374.3 231 69.4 -15.23 -114.7 -44.41 -49.98 -102.8 108.9 231.6 137 230.1 294.3 228 43.4 -114.2 -145.7 -191.4 54.02 -102.8 -103.1 -145.4 -200 -113.9 -53.66 -288 -143.6 33.77 -249.7 107.6 -191 -89.79 -92.1 -257.4 -116 -329.9 -82.66 -101 -90.6 108.8 -74.68 -146.4 -187 -81.79 -228.1 -46.35 -114 -13.85 -194.7 -164 -48.6 49.77 233.3 -85.41 -34.98 -25.79 -176.1 55.65 15.02 -18.85 -66.66 -166 1.399 96.77 90.32 -102.4 164 -23.79 -66.1 -135.4 -215 -102.9 -64.66 -196 -32.6 41.77 -57.68 -186.4 -91.98 -124.8 -169.1 -44.35 -182 -104.9 -161.7 -22.04 -311.6 -224.2 -248.7 -89.41 -55.98 -80.79 -100.1 -181.4 -0.9764 -146.9 -28.66 88.96 -180.6 -439.2 -266.7 -102.4 -142 -119.8 -135.1 -8.351 -154 27.15 -115.7 0.9611 -50.6 -86.23 -228.7 -94.6 15.83 79.02 9.71 -94.54 -72.17 -122 113.1 33.77 -169.8 -256.4 30.14 13.4 79.83 21.02 70.71 14.46 -24.17 22.96 130.1 121.8 84.21 -6.415
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) 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')
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Computing time
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R Server
Big Analytics Cloud Computing Center
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