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Data:
0.0013999990894105 0.0876771622176185 0.253920154859331 -0.0329407507841036 0.00427395855379008 -0.0889668134958849 -0.165281368150775 -0.33220433089751 0.219927126513421 0.236701919529402 0.234525437835356 -0.0304289243382355 0.0953913592916723 0.222671044416315 0.0914666084830592 -0.206034580848975 0.317983507811223 0.209887369047125 0.47505736015715 -0.222062521324981 0.0119408639911758 -0.172481528037084 -0.342720660187122 -0.343867756086308 0.088435724392957 0.207149951929822 0.0343760117343465 -0.301707672057424 -0.0110173444677831 -0.0691482212286872 -0.925838573157798 0.137673245546374 0.0727565374056945 0.201173883123843 0.0856783035058499 -0.109577212438584 -0.260551934204086 0.147885882041203 0.212413648519748 -0.0832851309957541 -0.115216896092894 -0.221533847078893 0.33793754507397 -0.134446101735477 0.164631582429395 0.0888222485881065 0.0469533479921545 0.241738416214495 -0.0752540261587926 -0.0299598595890917 0.225881103587503 0.0412636104361186 0.128108276344458 -0.141250457347375 0.481091241511557 -0.11016711751824 0.191801843198123 -0.066757083135655 0.0394798962983962 -0.0743907512944395 0.0864476825544102 -0.0321810065262648 0.121406171476103 -0.0784677853924106 -0.219288911182277 -0.0373619502368736 0.449190378168538 -0.083495394142657 0.104571843583723 0.564217976443139 -0.156913984252991 0.0837627464269975 0.129666943943497 -0.0279018187223396 -0.0344326851573588 -0.328655799589801 0.121385421475263 0.0494710079304724 0.428353319793773 -0.368885964978714 -0.302589451991359 -0.45541145100303 -0.0851366312257987 0.244537840690697
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,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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R Server
Big Analytics Cloud Computing Center
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