Autocorrelation function
| 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 | 7.746 | 0 |
| 1 | 0.084803 | 0.6569 | 0.256883 |
| 2 | -0.251332 | -1.9468 | 0.971878 |
| 3 | 0.135737 | 1.0514 | 0.148643 |
| 4 | -0.172403 | -1.3354 | 0.90661 |
| 5 | 0.037942 | 0.2939 | 0.384925 |
| 6 | 0.413133 | 3.2001 | 0.001098 |
| 7 | 0.042611 | 0.3301 | 0.37125 |
| 8 | -0.053069 | -0.4111 | 0.658757 |
| 9 | 0.009017 | 0.0698 | 0.472274 |
| 10 | -0.318328 | -2.4658 | 0.991726 |
| 11 | -0.005023 | -0.0389 | 0.515455 |
| 12 | 0.573405 | 4.4416 | 2e-05 |
| 13 | 0.020013 | 0.155 | 0.438663 |
| 14 | -0.247481 | -1.917 | 0.969997 |
| 15 | 0.010475 | 0.0811 | 0.467801 |
| 16 | -0.202805 | -1.5709 | 0.93927 |
| 17 | -0.052623 | -0.4076 | 0.657497 |
| 18 | 0.254644 | 1.9725 | 0.026585 |
| 19 | -0.005376 | -0.0416 | 0.516539 |
| 20 | -0.057557 | -0.4458 | 0.67134 |
| 21 | -0.025016 | -0.1938 | 0.576497 |
| 22 | -0.300842 | -2.3303 | 0.988415 |
| 23 | -0.038143 | -0.2955 | 0.615666 |
| 24 | 0.343503 | 2.6608 | 0.004994 |
| 25 | -0.009406 | -0.0729 | 0.528919 |
| 26 | -0.232718 | -1.8026 | 0.961765 |
| 27 | -0.092428 | -0.7159 | 0.761598 |
| 28 | -0.167166 | -1.2949 | 0.899835 |
| 29 | -0.120875 | -0.9363 | 0.823562 |
| 30 | 0.093164 | 0.7216 | 0.236659 |
| 31 | -0.031421 | -0.2434 | 0.595733 |
| 32 | -0.109207 | -0.8459 | 0.799519 |
| 33 | -0.035521 | -0.2751 | 0.607926 |
| 34 | -0.197414 | -1.5292 | 0.93426 |
| 35 | -0.094692 | -0.7335 | 0.766939 |
| 36 | 0.25761 | 1.9954 | 0.025271 |
| 37 | 0.025267 | 0.1957 | 0.422746 |
| 38 | -0.125946 | -0.9756 | 0.833403 |
| 39 | 0.02295 | 0.1778 | 0.429751 |
| 40 | -0.104007 | -0.8056 | 0.788182 |
| 41 | -0.062368 | -0.4831 | 0.684608 |
| 42 | 0.085324 | 0.6609 | 0.255597 |
| 43 | 0.049856 | 0.3862 | 0.350363 |
| 44 | -0.009362 | -0.0725 | 0.528784 |
| 45 | 0.024729 | 0.1915 | 0.424371 |
| 46 | -0.075344 | -0.5836 | 0.719167 |
| 47 | -0.065591 | -0.5081 | 0.693366 |
| 48 | 0.169196 | 1.3106 | 0.097497 |
| 49 | 0.036243 | 0.2807 | 0.38994 |
| 50 | -0.067217 | -0.5207 | 0.697742 |
| 51 | 0.083396 | 0.646 | 0.260378 |
| 52 | -0.069073 | -0.535 | 0.7027 |
| 53 | -0.05575 | -0.4318 | 0.666298 |
| 54 | 0.043212 | 0.3347 | 0.369503 |
| 55 | 0.018512 | 0.1434 | 0.443229 |
| 56 | 0.046781 | 0.3624 | 0.359176 |
| 57 | 0.04806 | 0.3723 | 0.355503 |
| 58 | 0.019172 | 0.1485 | 0.441221 |
| 59 | 0.006752 | 0.0523 | 0.479232 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.084803 | 0.6569 | 0.256883 |
| 1 | -0.260396 | -2.017 | 0.975913 |
| 2 | 0.199552 | 1.5457 | 0.063715 |
| 3 | -0.316996 | -2.4554 | 0.991507 |
| 4 | 0.270583 | 2.0959 | 0.020157 |
| 5 | 0.216728 | 1.6788 | 0.049199 |
| 6 | 0.101453 | 0.7859 | 0.217523 |
| 7 | 0.050459 | 0.3909 | 0.348644 |
| 8 | -0.061959 | -0.4799 | 0.683489 |
| 9 | -0.279083 | -2.1618 | 0.982682 |
| 10 | 0.049323 | 0.3821 | 0.351886 |
| 11 | 0.439393 | 3.4035 | 0.000595 |
| 12 | -0.119555 | -0.9261 | 0.820939 |
| 13 | -0.154959 | -1.2003 | 0.882631 |
| 14 | -0.118168 | -0.9153 | 0.818157 |
| 15 | 0.054141 | 0.4194 | 0.338221 |
| 16 | -0.027324 | -0.2116 | 0.583451 |
| 17 | -0.082927 | -0.6424 | 0.738454 |
| 18 | -0.071658 | -0.5551 | 0.709541 |
| 19 | -0.019263 | -0.1492 | 0.559055 |
| 20 | 0.062901 | 0.4872 | 0.313934 |
| 21 | -0.010324 | -0.08 | 0.531737 |
| 22 | -0.005715 | -0.0443 | 0.51758 |
| 23 | -0.070649 | -0.5472 | 0.70688 |
| 24 | 0.005873 | 0.0455 | 0.481934 |
| 25 | -0.134122 | -1.0389 | 0.848491 |
| 26 | -0.106154 | -0.8223 | 0.79291 |
| 27 | -0.019018 | -0.1473 | 0.558309 |
| 28 | -0.133394 | -1.0333 | 0.847187 |
| 29 | -0.085806 | -0.6647 | 0.745591 |
| 30 | -0.116598 | -0.9032 | 0.814975 |
| 31 | -0.048156 | -0.373 | 0.644774 |
| 32 | 0.037938 | 0.2939 | 0.384936 |
| 33 | 0.061781 | 0.4786 | 0.316997 |
| 34 | -0.077407 | -0.5996 | 0.724483 |
| 35 | 0.04548 | 0.3523 | 0.362928 |
| 36 | -0.004445 | -0.0344 | 0.513676 |
| 37 | 0.185866 | 1.4397 | 0.077573 |
| 38 | 0.044415 | 0.344 | 0.366011 |
| 39 | -0.100761 | -0.7805 | 0.780916 |
| 40 | -0.034944 | -0.2707 | 0.606216 |
| 41 | -0.094471 | -0.7318 | 0.76642 |
| 42 | 0.175112 | 1.3564 | 0.090025 |
| 43 | -0.106647 | -0.8261 | 0.793985 |
| 44 | 0.004666 | 0.0361 | 0.485643 |
| 45 | -0.096751 | -0.7494 | 0.771737 |
| 46 | 0.076696 | 0.5941 | 0.277345 |
| 47 | -0.004547 | -0.0352 | 0.51399 |
| 48 | -0.067476 | -0.5227 | 0.698435 |
| 49 | 0.009714 | 0.0752 | 0.470136 |
| 50 | -0.022487 | -0.1742 | 0.568846 |
| 51 | 0.00789 | 0.0611 | 0.475735 |
| 52 | -0.046519 | -0.3603 | 0.640068 |
| 53 | -0.087259 | -0.6759 | 0.749152 |
| 54 | -0.016552 | -0.1282 | 0.550794 |
| 55 | 0.005645 | 0.0437 | 0.482634 |
| 56 | -0.058597 | -0.4539 | 0.674227 |
| 57 | 0.066191 | 0.5127 | 0.305018 |
| 58 | -0.00284 | -0.022 | 0.50874 |
| 59 | NA | NA | NA |
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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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