Autocorrelation Function (industriële productie cokes, geraff. aardolie...)
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 2 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 7.2801 | 0 |
| 1 | 0.262783 | 1.9131 | 0.030573 |
| 2 | 0.096099 | 0.6996 | 0.243614 |
| 3 | -0.102981 | -0.7497 | 0.771629 |
| 4 | -0.319563 | -2.3265 | 0.988075 |
| 5 | -0.319684 | -2.3273 | 0.9881 |
| 6 | -0.161082 | -1.1727 | 0.876919 |
| 7 | -0.005578 | -0.0406 | 0.516119 |
| 8 | 0.127384 | 0.9274 | 0.178969 |
| 9 | 0.156663 | 1.1405 | 0.129598 |
| 10 | 0.086841 | 0.6322 | 0.264982 |
| 11 | 0.020238 | 0.1473 | 0.441714 |
| 12 | -0.030643 | -0.2231 | 0.587837 |
| 13 | -0.080926 | -0.5891 | 0.720868 |
| 14 | -0.068135 | -0.496 | 0.689038 |
| 15 | -0.053014 | -0.3859 | 0.64946 |
| 16 | 0.009261 | 0.0674 | 0.473249 |
| 17 | 0.067108 | 0.4886 | 0.313587 |
| 18 | -0.005639 | -0.0411 | 0.516296 |
| 19 | -0.004467 | -0.0325 | 0.512912 |
| 20 | 0.088579 | 0.6449 | 0.260899 |
| 21 | 0.06986 | 0.5086 | 0.306574 |
| 22 | -0.009269 | -0.0675 | 0.526773 |
| 23 | -0.07067 | -0.5145 | 0.695475 |
| 24 | -0.09639 | -0.7017 | 0.75704 |
| 25 | -0.089132 | -0.6489 | 0.740394 |
| 26 | -0.041886 | -0.3049 | 0.619194 |
| 27 | 0.034501 | 0.2512 | 0.401328 |
| 28 | 0.039338 | 0.2864 | 0.38785 |
| 29 | 0.021947 | 0.1598 | 0.436831 |
| 30 | -0.016533 | -0.1204 | 0.547675 |
| 31 | -0.052833 | -0.3846 | 0.648974 |
| 32 | -0.042698 | -0.3108 | 0.621431 |
| 33 | 0.002419 | 0.0176 | 0.493009 |
| 34 | 0.026173 | 0.1905 | 0.424807 |
| 35 | 0.02149 | 0.1565 | 0.438135 |
| 36 | 0.020106 | 0.1464 | 0.442092 |
| 37 | 0.012614 | 0.0918 | 0.463588 |
| 38 | -0.015451 | -0.1125 | 0.544569 |
| 39 | -0.036886 | -0.2685 | 0.605334 |
| 40 | -0.034361 | -0.2502 | 0.598281 |
| 41 | -0.013729 | -0.0999 | 0.539618 |
| 42 | 0.011896 | 0.0866 | 0.465655 |
| 43 | 0.026586 | 0.1936 | 0.423634 |
| 44 | 0.022747 | 0.1656 | 0.43455 |
| 45 | 0.002452 | 0.0179 | 0.492912 |
| 46 | -0.014975 | -0.109 | 0.543202 |
| 47 | -0.017997 | -0.131 | 0.551871 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.262783 | 1.9131 | 0.030573 |
| 1 | 0.02905 | 0.2115 | 0.416658 |
| 2 | -0.145281 | -1.0577 | 0.852497 |
| 3 | -0.28668 | -2.0871 | 0.979146 |
| 4 | -0.190496 | -1.3868 | 0.914351 |
| 5 | -0.022451 | -0.1634 | 0.564606 |
| 6 | 0.027348 | 0.1991 | 0.421474 |
| 7 | 0.028081 | 0.2044 | 0.419401 |
| 8 | -0.022184 | -0.1615 | 0.563843 |
| 9 | -0.064593 | -0.4702 | 0.679945 |
| 10 | -0.024648 | -0.1794 | 0.570863 |
| 11 | 0.01915 | 0.1394 | 0.444825 |
| 12 | -0.007516 | -0.0547 | 0.521714 |
| 13 | -0.015946 | -0.1161 | 0.545991 |
| 14 | -0.043651 | -0.3178 | 0.624052 |
| 15 | 0.007622 | 0.0555 | 0.47798 |
| 16 | 0.046846 | 0.341 | 0.367208 |
| 17 | -0.071775 | -0.5225 | 0.69826 |
| 18 | -0.041768 | -0.3041 | 0.61887 |
| 19 | 0.115716 | 0.8424 | 0.201668 |
| 20 | 0.089281 | 0.65 | 0.259257 |
| 21 | -0.049038 | -0.357 | 0.638747 |
| 22 | -0.115936 | -0.844 | 0.798776 |
| 23 | -0.05205 | -0.3789 | 0.646872 |
| 24 | 0.018368 | 0.1337 | 0.447064 |
| 25 | 0.03101 | 0.2258 | 0.411129 |
| 26 | 0.018281 | 0.1331 | 0.447314 |
| 27 | -0.077399 | -0.5635 | 0.712256 |
| 28 | -0.096494 | -0.7025 | 0.757274 |
| 29 | -0.051963 | -0.3783 | 0.646638 |
| 30 | -0.012257 | -0.0892 | 0.535385 |
| 31 | 0.005429 | 0.0395 | 0.484311 |
| 32 | 0.005824 | 0.0424 | 0.48317 |
| 33 | -0.015328 | -0.1116 | 0.544216 |
| 34 | -0.033579 | -0.2445 | 0.59609 |
| 35 | -0.033875 | -0.2466 | 0.59692 |
| 36 | -0.023015 | -0.1676 | 0.566214 |
| 37 | -0.001673 | -0.0122 | 0.504837 |
| 38 | 0.010879 | 0.0792 | 0.468585 |
| 39 | -0.011653 | -0.0848 | 0.533643 |
| 40 | -0.041501 | -0.3021 | 0.618133 |
| 41 | -0.028629 | -0.2084 | 0.582152 |
| 42 | 0.004259 | 0.031 | 0.487692 |
| 43 | 0.015978 | 0.1163 | 0.453918 |
| 44 | 0.004879 | 0.0355 | 0.4859 |
| 45 | -0.016579 | -0.1207 | 0.547805 |
| 46 | -0.029689 | -0.2161 | 0.585144 |
| 47 | -0.025355 | -0.1846 | 0.572872 |
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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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