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Data:
0.0976999042077651 0.0888998338005906 0.0964998334679063 0.0894998341831167 0.0853998438657789 0.0842998482610295 0.0836998497391663 0.0861998477733592 0.0906998413388313 0.0956998327961495 0.095599828849401 0.0969998524271482 -0.357053321260274 -1.64125592174998 -5.65398562941077 -3.57369093017378 3.08909186160842 -1.14294096349874 0.602499286559887 0.166822064597347 -2.02531351834469 -1.51546107389643 0.0724805852747878 -2.12205929540466 2.19628766173121 -0.0605079650571812 3.64463341109045 4.3312299372585 2.20072805798757 2.74253723998927 2.80660340500022 2.42310540764882 -1.664502969128 1.80722045286534 -0.0879161686365307 -0.790544927696883 3.70734995450548 2.45145951417313 0.471526918886696 3.41122052361345 0.187591405261646 1.87810105953099 4.50494118727212 1.9031677843587 1.52467514737968 1.72575668554543 -3.58861424781686 6.96148170420439 1.69928770794081 3.04405183004109 2.48606121085736 -0.807396900146041 1.53657589478381 -1.38161897704946 2.77598793097358 -5.93296184347235 -0.263889052860067 -3.02535674547065 4.0012575934818 4.32801939555623 -2.04085540667922 6.68357991579588 -2.06004642382633 3.522754668406 -3.36850476440946 -0.711479229976281 -1.61039660855169 -1.94921198196442 -1.21319020567838 -2.44756150052018 -1.41356427888564 5.47243063881544 1.89600852094512 3.88035094112904 0.922325836564759 -3.08706584611076 1.06860614455855 0.881413679492219 -1.57557832654175 0.527939913027753 -3.86831652628551 1.40765149496435 -1.40354501070749 -1.55576677799834 0.914900847927305 2.99711419297897 1.32395378714095 2.39203609300244 -0.726259896583282 2.29567767446795 -2.53527156152751 3.74269940625232 -1.86879168954026 4.51712030941218 4.45668746161051 1.59689203792215 3.26315868802412 -0.543077103435425 -3.46548932688626 0.0598501475597718 -3.52257936759703 0.879330630495331 0.808267955488759 -0.891900331296483 0.443994317595385 0.141298011897711 -2.80826062918138 5.52761466380316 2.17891836739269 0.581613942878553 2.24179847901961 -2.07361858024247 4.4969886566
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
FALSE
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)
1
0
1
2
Seasonality
12
12
1
2
3
4
6
12
CI type
0
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 <- 'Default' 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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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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