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Data:
-2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -0.0067221197756271 1.8651746813703E-14 0.0067221197756187 -0.012775191488727 0.012775191488696 -0.0067221197756271 1.8651746813703E-14 1.8651746813703E-14 0.0067221197756187 -2.7089441800854E-14 -0.0067221197756271 0.0067221197756187 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -0.0067221197756271 1.8651746813703E-14 1.8651746813703E-14 0.0017733645364189 3.1974423109205E-14 3.1974423109205E-14 -0.0063988842582678 1.0658141036402E-14 0.0063988842583105 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 -0.0063988842582678 0.0063988842583105 -0.0063988842582678 0.0063988842583105 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 -0.0063988842582678 1.0658141036402E-14 0.0063988842583105 -0.0063988842582678 0.0063988842583105 -0.0063988842582678 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 0.0063988842583105 -0.0063988842582678 1.0658141036402E-14 0.0063988842583105 -0.0063988842582678 1.0658141036402E-14 1.0658141036402E-14 0.0063988842583105 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 0.0003542958407321 -0.0063966102966977 -5.3290705182008E-15 -5.3290705182008E-15 -5.3290705182008E-15 0.0063966102966946 2.2204460492503E-15 -0.0039041753228979 3.5971225997855E-14 3.5971225997855E-14 3.5971225997855E-14 3.5971225997855E-14 3.5971225997855E-14 3.5971225997855E-14 -0.0064217131136641 -1.5987211554602E-14 -1.5987211554602E-14 -1.5987211554602E-14 -1.5987211554602E-14 0.0064217131136841 3.5971225997855E-14 3.5971225997855E-14 3.5971225997855E-14 -0.0064217131136641 0.0064217131136841 3.5971225997855E-14 3.5971225997855E-14 -0.017578928007164 0.0064935293105748 2.6201263381154E-14 2.6201263381154E-14 2.6201263381154E-14 2.6201263381154E-14 2.6201263381154E-14 -0.0064935293105735 -2.4868995751604E-14 -0.011649203449725 -1.3322676295502E-14 -0.0058758891057136 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 0.0058758891056558 -0.0058758891057136 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 0.0058758891056558 -0.0058758891057136 -4.4408920985006E-14 0.0058758891056558 -1.3322676295502E-14 -1.3322676295502E-14 -0.0058758891057136 -4.4408920985006E-14 0.0058758891056558 -1.3322676295502E-14 -1.3322676295502E-14 -1.3322676295502E-14 -1.3322676295502E-14 -1.3322676295502E-14 -1.3322676295502E-14 -0.0058758891057136 0.0058758891056558 -0.0058758891057136 0.0058758891056558 -0.0058758891057136 0.0058758891056558 -0.0058758891057136 0.0058758891056558 -1.3322676295502E-14 -1.3322676295502E-14 -0.0058758891057136 0.0058758891056558 -0.0058758891057136 -4.4408920985006E-14 -4.4408920985006E-14 -4.4408920985006E-14 0.0058758891056558 -1.3322676295502E-14 -0.011416069481314 -2.9309887850104E-14 0.0055401803755708 0.010260258569556 -1.1990408665952E-14 -1.1990408665952E-14 -1.1990408665952E-14 -0.035625288710512 -9.7699626167014E-15 -9.7699626167014E-15 0.01982484976529 -2.9309887850104E-14 -2.9309887850104E-14 -0.019824849765329 0.01982484976529 -2.9309887850104E-14 -2.9309887850104E-14 -2.9309887850104E-14 -2.9309887850104E-14 0.015800438945171 -1.1990408665952E-14 -1.1990408665952E-14 -1.1990408665952E-14 -1.1990408665952E-14 -1.1990408665952E-14 -1.1990408665952E-14
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
Apollo Oil Corp.
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)
Time series of Xycoon Stock Exchange
0
1
2
Degree of seasonal differencing (D)
No season
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 (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF') 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')
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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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