Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
1.57374261731945e-11 -7.78930535948257e-10 -9.9175708985985e-11 -6.17827740269557e-10 2.80917788736100e-10 -1.99763641625773e-10 -5.62280802209156e-10 -3.834963850376e-10 -4.17216827233602e-10 -5.67882584539982e-10 2.58637700997753e-10 -1.90527862999576e-10 -1.71577262349281e-09 -3.35381605481246e-10 -8.96626940018993e-10 -4.10744385478952e-10 -1.49797381174457e-10 -1.15741365009681e-09 4.97758749117844e-10 5.61962186332171e-10 -8.28837144782243e-11 3.00919401758308e-10 -3.56842310474036e-10 -2.65445564649461e-10 1.41088100275876e-09 -1.03460467298531e-10 7.3845130614864e-11 -9.69944238759222e-11 5.131852651389e-10 -7.01634738041197e-10 -1.38560835918767e-10 -7.71959620063928e-10 -7.0799159060927e-10 9.21374878678968e-10 -2.34800895738451e-10 -1.51898308298110e-10 4.14446046879670e-10 1.60789644435293e-10 -5.59704597759589e-10 -1.62599611179260e-10 -2.51259688470892e-10 6.02392608325336e-10 1.40768937817837e-09 8.67663665864935e-10 2.48443566634038e-09 -1.58728692714916e-10 -2.38522160490848e-10 1.69115939544137e-09 4.90955643343371e-09 -4.72645086787659e-11 -8.42090559114202e-10 -9.78841734822696e-10 5.33492432359219e-10 9.43414526915943e-10 3.41369607077995e-10 1.95723637461107e-10 3.00517747585271e-10 -9.1302653563983e-10 -8.67566852412382e-10 5.08551441993418e-10 -4.81913846464095e-09 8.72479910544946e-10 4.49296782533904e-10 5.3731256719039e-10 -7.49441166608007e-10 7.73568132392035e-10 1.67536877808481e-10 2.03569599537960e-09 5.86636632995461e-11 -6.17168126334066e-10 -3.4433140227747e-10 -8.6322485209947e-10 -3.25333745267874e-09 3.08292515576174e-10 1.19026377148694e-09 9.77788786184594e-10 -4.57474202748115e-11 -1.02595722322947e-10 -9.7138521570781e-10 1.17167162511307e-09 -1.15353929661592e-09 -8.03674125843833e-10 6.27537221374364e-10 -8.74606140260688e-10 1.5572731935976e-09
Sample Range:
(leave blank to include all observations)
From:
To:
Number of time lags
60
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
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 (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='lags',ylab='ACF') 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 1:par1) { 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(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-1,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(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
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation