Autocorrelation Function (energieprijzen)
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 5 seconds |
| R Server | 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 7.2801 | 0 |
| 1 | 0.009179 | 0.0668 | 0.473486 |
| 2 | -0.26418 | -1.9233 | 0.970087 |
| 3 | -0.037692 | -0.2744 | 0.607578 |
| 4 | 0.030848 | 0.2246 | 0.411587 |
| 5 | 0.103671 | 0.7547 | 0.226874 |
| 6 | -0.151239 | -1.101 | 0.862072 |
| 7 | -0.048704 | -0.3546 | 0.63784 |
| 8 | 0.264896 | 1.9285 | 0.02958 |
| 9 | -0.130678 | -0.9513 | 0.827127 |
| 10 | -0.23774 | -1.7308 | 0.955344 |
| 11 | -0.165775 | -1.2069 | 0.883577 |
| 12 | 0.21434 | 1.5604 | 0.062306 |
| 13 | 0.061175 | 0.4454 | 0.328937 |
| 14 | -0.219197 | -1.5958 | 0.941758 |
| 15 | -0.148426 | -1.0806 | 0.857607 |
| 16 | 0.137097 | 0.9981 | 0.161388 |
| 17 | 0.123548 | 0.8994 | 0.186243 |
| 18 | -0.094427 | -0.6874 | 0.752597 |
| 19 | -0.059592 | -0.4338 | 0.666916 |
| 20 | 0.078511 | 0.5716 | 0.285016 |
| 21 | 0.074265 | 0.5407 | 0.295505 |
| 22 | -0.017547 | -0.1277 | 0.550583 |
| 23 | -0.019422 | -0.1414 | 0.555953 |
| 24 | 0.183526 | 1.3361 | 0.093615 |
| 25 | 0.057602 | 0.4193 | 0.338329 |
| 26 | -0.028483 | -0.2074 | 0.581737 |
| 27 | -0.100788 | -0.7337 | 0.766831 |
| 28 | -0.093262 | -0.679 | 0.749941 |
| 29 | 0.128512 | 0.9356 | 0.176866 |
| 30 | -0.05341 | -0.3888 | 0.650521 |
| 31 | -0.077899 | -0.5671 | 0.713485 |
| 32 | 0.039344 | 0.2864 | 0.387834 |
| 33 | -0.023792 | -0.1732 | 0.568427 |
| 34 | -0.01468 | -0.1069 | 0.542353 |
| 35 | -0.055473 | -0.4038 | 0.656026 |
| 36 | -0.006747 | -0.0491 | 0.519497 |
| 37 | 0.004692 | 0.0342 | 0.486439 |
| 38 | -0.057406 | -0.4179 | 0.661152 |
| 39 | 0.004452 | 0.0324 | 0.487132 |
| 40 | 0.010203 | 0.0743 | 0.470535 |
| 41 | 0.010121 | 0.0737 | 0.470771 |
| 42 | 0.007351 | 0.0535 | 0.47876 |
| 43 | 0.004818 | 0.0351 | 0.486077 |
| 44 | 0.015147 | 0.1103 | 0.456304 |
| 45 | 0.009347 | 0.068 | 0.473002 |
| 46 | -0.000152 | -0.0011 | 0.500439 |
| 47 | 0.005777 | 0.0421 | 0.483306 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.009179 | 0.0668 | 0.473486 |
| 1 | -0.264287 | -1.924 | 0.970136 |
| 2 | -0.034621 | -0.252 | 0.599009 |
| 3 | -0.041396 | -0.3014 | 0.617843 |
| 4 | 0.091163 | 0.6637 | 0.254886 |
| 5 | -0.169095 | -1.231 | 0.888127 |
| 6 | 0.006873 | 0.05 | 0.480142 |
| 7 | 0.208482 | 1.5178 | 0.067507 |
| 8 | -0.179806 | -1.309 | 0.901911 |
| 9 | -0.151119 | -1.1002 | 0.861884 |
| 10 | -0.226449 | -1.6486 | 0.947424 |
| 11 | 0.154699 | 1.1262 | 0.132573 |
| 12 | -0.141412 | -1.0295 | 0.846038 |
| 13 | -0.102133 | -0.7435 | 0.769781 |
| 14 | -0.19963 | -1.4533 | 0.923986 |
| 15 | 0.032044 | 0.2333 | 0.408219 |
| 16 | 0.024572 | 0.1789 | 0.429353 |
| 17 | -0.045281 | -0.3297 | 0.628519 |
| 18 | 0.015893 | 0.1157 | 0.454164 |
| 19 | -0.158668 | -1.1551 | 0.873389 |
| 20 | 0.054107 | 0.3939 | 0.347616 |
| 21 | -0.008444 | -0.0615 | 0.524394 |
| 22 | 0.131579 | 0.9579 | 0.171228 |
| 23 | 0.002438 | 0.0178 | 0.492952 |
| 24 | -0.031823 | -0.2317 | 0.591158 |
| 25 | 0.113055 | 0.8231 | 0.207082 |
| 26 | -0.033696 | -0.2453 | 0.596418 |
| 27 | -0.095012 | -0.6917 | 0.753927 |
| 28 | -0.032431 | -0.2361 | 0.592868 |
| 29 | -0.05639 | -0.4105 | 0.658461 |
| 30 | -0.02969 | -0.2161 | 0.585148 |
| 31 | 0.017703 | 0.1289 | 0.448971 |
| 32 | -0.001431 | -0.0104 | 0.504137 |
| 33 | -0.043486 | -0.3166 | 0.623598 |
| 34 | 0.029879 | 0.2175 | 0.414319 |
| 35 | 0.022553 | 0.1642 | 0.435103 |
| 36 | -0.076738 | -0.5587 | 0.710627 |
| 37 | -0.078613 | -0.5723 | 0.715234 |
| 38 | -0.023515 | -0.1712 | 0.567638 |
| 39 | 0.004241 | 0.0309 | 0.487742 |
| 40 | -0.089414 | -0.6509 | 0.741052 |
| 41 | -0.035774 | -0.2604 | 0.602234 |
| 42 | -0.036075 | -0.2626 | 0.603073 |
| 43 | -0.009119 | -0.0664 | 0.526342 |
| 44 | -0.021504 | -0.1565 | 0.561903 |
| 45 | -0.049074 | -0.3573 | 0.638842 |
| 46 | -0.055227 | -0.4021 | 0.655371 |
| 47 | -0.119432 | -0.8695 | 0.805747 |
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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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