ACF/PACF Timeserie 1: Inschrijving aantal personenwagens
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 1 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 6.9282 | 0 |
| 1 | 0.278479 | 1.9294 | 0.029805 |
| 2 | 0.346261 | 2.399 | 0.010187 |
| 3 | 0.274912 | 1.9046 | 0.031414 |
| 4 | 0.234592 | 1.6253 | 0.055324 |
| 5 | 0.156935 | 1.0873 | 0.141171 |
| 6 | 0.125934 | 0.8725 | 0.19364 |
| 7 | -0.073817 | -0.5114 | 0.694298 |
| 8 | -0.056863 | -0.394 | 0.652322 |
| 9 | -0.046666 | -0.3233 | 0.626067 |
| 10 | -0.212374 | -1.4714 | 0.926141 |
| 11 | -0.117192 | -0.8119 | 0.78958 |
| 12 | -0.33199 | -2.3001 | 0.987083 |
| 13 | -0.261646 | -1.8127 | 0.961936 |
| 14 | -0.156744 | -1.086 | 0.858538 |
| 15 | -0.119643 | -0.8289 | 0.794371 |
| 16 | -0.333579 | -2.3111 | 0.987417 |
| 17 | -0.038483 | -0.2666 | 0.604547 |
| 18 | -0.097785 | -0.6775 | 0.749322 |
| 19 | -0.049198 | -0.3409 | 0.63265 |
| 20 | 0.063353 | 0.4389 | 0.331345 |
| 21 | -0.003075 | -0.0213 | 0.508455 |
| 22 | 0.004194 | 0.0291 | 0.488471 |
| 23 | 0.072037 | 0.4991 | 0.309998 |
| 24 | -0.124838 | -0.8649 | 0.804302 |
| 25 | -0.006095 | -0.0422 | 0.516754 |
| 26 | 0.029623 | 0.2052 | 0.41913 |
| 27 | -0.041527 | -0.2877 | 0.612595 |
| 28 | -0.013017 | -0.0902 | 0.535741 |
| 29 | -0.046384 | -0.3214 | 0.625332 |
| 30 | -0.064881 | -0.4495 | 0.672455 |
| 31 | -0.001441 | -0.01 | 0.503963 |
| 32 | -0.003292 | -0.0228 | 0.50905 |
| 33 | -0.014142 | -0.098 | 0.538821 |
| 34 | -0.007801 | -0.054 | 0.521438 |
| 35 | -0.023369 | -0.1619 | 0.563969 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.278479 | 1.9294 | 0.029805 |
| 1 | 0.291301 | 2.0182 | 0.024589 |
| 2 | 0.148611 | 1.0296 | 0.154177 |
| 3 | 0.072852 | 0.5047 | 0.308027 |
| 4 | -0.013761 | -0.0953 | 0.537778 |
| 5 | -0.018136 | -0.1257 | 0.549734 |
| 6 | -0.21575 | -1.4948 | 0.929239 |
| 7 | -0.107554 | -0.7452 | 0.770094 |
| 8 | 0.01674 | 0.116 | 0.454078 |
| 9 | -0.160072 | -1.109 | 0.863525 |
| 10 | 0.016383 | 0.1135 | 0.455052 |
| 11 | -0.224438 | -1.555 | 0.936736 |
| 12 | -0.090216 | -0.625 | 0.732545 |
| 13 | 0.09638 | 0.6677 | 0.25375 |
| 14 | 0.105999 | 0.7344 | 0.233143 |
| 15 | -0.221768 | -1.5365 | 0.934503 |
| 16 | 0.123435 | 0.8552 | 0.19835 |
| 17 | 0.071727 | 0.4969 | 0.31075 |
| 18 | -0.050111 | -0.3472 | 0.635012 |
| 19 | 0.0666 | 0.4614 | 0.32329 |
| 20 | 3.8e-05 | 3e-04 | 0.499895 |
| 21 | -0.101215 | -0.7012 | 0.75673 |
| 22 | -0.058879 | -0.4079 | 0.657429 |
| 23 | -0.312731 | -2.1667 | 0.982372 |
| 24 | -0.050745 | -0.3516 | 0.636651 |
| 25 | 0.05416 | 0.3752 | 0.354569 |
| 26 | 0.087143 | 0.6037 | 0.274429 |
| 27 | -0.106148 | -0.7354 | 0.767168 |
| 28 | -0.062255 | -0.4313 | 0.665915 |
| 29 | 0.083864 | 0.581 | 0.28197 |
| 30 | 0.048819 | 0.3382 | 0.368333 |
| 31 | -0.038729 | -0.2683 | 0.605199 |
| 32 | 0.148475 | 1.0287 | 0.154396 |
| 33 | -0.076336 | -0.5289 | 0.700333 |
| 34 | -0.106279 | -0.7363 | 0.767442 |
| 35 | -0.100038 | -0.6931 | 0.754202 |
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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')
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