Methode 2 op tijdreeks 3
| 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 | 7.746 | 0 |
| 1 | 0.465111 | 3.6027 | 0.00032 |
| 2 | 0.430663 | 3.3359 | 0.000731 |
| 3 | 0.358433 | 2.7764 | 0.003661 |
| 4 | 0.220381 | 1.7071 | 0.04649 |
| 5 | 0.330588 | 2.5607 | 0.00649 |
| 6 | 0.244521 | 1.8941 | 0.031521 |
| 7 | 0.197855 | 1.5326 | 0.065318 |
| 8 | 0.088027 | 0.6819 | 0.248976 |
| 9 | -0.113331 | -0.8779 | 0.808238 |
| 10 | -0.156789 | -1.2145 | 0.885339 |
| 11 | -0.171197 | -1.3261 | 0.905082 |
| 12 | 0.02883 | 0.2233 | 0.412025 |
| 13 | -0.194653 | -1.5078 | 0.93157 |
| 14 | -0.250234 | -1.9383 | 0.971352 |
| 15 | -0.158406 | -1.227 | 0.887694 |
| 16 | -0.309211 | -2.3951 | 0.990124 |
| 17 | -0.1178 | -0.9125 | 0.817416 |
| 18 | -0.174969 | -1.3553 | 0.9098 |
| 19 | -0.131848 | -1.0213 | 0.844392 |
| 20 | -0.035299 | -0.2734 | 0.607268 |
| 21 | -0.162441 | -1.2583 | 0.893415 |
| 22 | -0.115544 | -0.895 | 0.812818 |
| 23 | 0.009853 | 0.0763 | 0.469709 |
| 24 | 0.066334 | 0.5138 | 0.304632 |
| 25 | 0.001921 | 0.0149 | 0.494089 |
| 26 | -0.052588 | -0.4073 | 0.657397 |
| 27 | -0.020184 | -0.1563 | 0.561855 |
| 28 | -0.11111 | -0.8607 | 0.803572 |
| 29 | -0.051582 | -0.3995 | 0.654546 |
| 30 | -0.114316 | -0.8855 | 0.810285 |
| 31 | -0.116079 | -0.8991 | 0.813914 |
| 32 | -0.070299 | -0.5445 | 0.705953 |
| 33 | -0.28148 | -2.1803 | 0.983417 |
| 34 | -0.18794 | -1.4558 | 0.924666 |
| 35 | -0.151124 | -1.1706 | 0.876806 |
| 36 | -0.142139 | -1.101 | 0.862354 |
| 37 | -0.128052 | -0.9919 | 0.837381 |
| 38 | -0.128073 | -0.992 | 0.837421 |
| 39 | -0.101125 | -0.7833 | 0.781738 |
| 40 | -0.091795 | -0.711 | 0.760093 |
| 41 | -0.047539 | -0.3682 | 0.643004 |
| 42 | -0.032156 | -0.2491 | 0.597926 |
| 43 | -0.022944 | -0.1777 | 0.570231 |
| 44 | 0.036629 | 0.2837 | 0.388797 |
| 45 | -0.01484 | -0.1149 | 0.545565 |
| 46 | 0.045071 | 0.3491 | 0.364112 |
| 47 | 0.123902 | 0.9597 | 0.170518 |
| 48 | 0.102227 | 0.7919 | 0.215784 |
| 49 | 0.108194 | 0.8381 | 0.202659 |
| 50 | 0.086107 | 0.667 | 0.253671 |
| 51 | 0.086199 | 0.6677 | 0.253444 |
| 52 | 0.081937 | 0.6347 | 0.264026 |
| 53 | 0.081394 | 0.6305 | 0.265389 |
| 54 | 0.066043 | 0.5116 | 0.305417 |
| 55 | 0.06127 | 0.4746 | 0.318399 |
| 56 | 0.042305 | 0.3277 | 0.372141 |
| 57 | 0.027334 | 0.2117 | 0.416517 |
| 58 | 0.0243 | 0.1882 | 0.425668 |
| 59 | 0.041657 | 0.3227 | 0.374031 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.465111 | 3.6027 | 0.00032 |
| 1 | 0.2735 | 2.1185 | 0.01914 |
| 2 | 0.118203 | 0.9156 | 0.181772 |
| 3 | -0.062538 | -0.4844 | 0.685074 |
| 4 | 0.200269 | 1.5513 | 0.063047 |
| 5 | 0.025838 | 0.2001 | 0.421024 |
| 6 | -0.030766 | -0.2383 | 0.593775 |
| 7 | -0.150351 | -1.1646 | 0.875608 |
| 8 | -0.262968 | -2.0369 | 0.976963 |
| 9 | -0.170255 | -1.3188 | 0.903875 |
| 10 | -0.040494 | -0.3137 | 0.62257 |
| 11 | 0.32448 | 2.5134 | 0.00733 |
| 12 | -0.239124 | -1.8522 | 0.965544 |
| 13 | -0.201676 | -1.5622 | 0.938247 |
| 14 | 0.19489 | 1.5096 | 0.068195 |
| 15 | 0.018406 | 0.1426 | 0.443553 |
| 16 | 0.07376 | 0.5713 | 0.284951 |
| 17 | -0.160517 | -1.2434 | 0.890714 |
| 18 | -0.02957 | -0.229 | 0.590194 |
| 19 | 0.101289 | 0.7846 | 0.217894 |
| 20 | 0.062774 | 0.4862 | 0.314283 |
| 21 | -0.096129 | -0.7446 | 0.770293 |
| 22 | 0.034142 | 0.2645 | 0.396165 |
| 23 | 0.038375 | 0.2973 | 0.38365 |
| 24 | -0.08115 | -0.6286 | 0.733998 |
| 25 | -0.093839 | -0.7269 | 0.764934 |
| 26 | -0.13625 | -1.0554 | 0.85226 |
| 27 | -0.077551 | -0.6007 | 0.72485 |
| 28 | -0.053385 | -0.4135 | 0.659648 |
| 29 | -0.099135 | -0.7679 | 0.77722 |
| 30 | -0.048275 | -0.3739 | 0.645116 |
| 31 | -0.024999 | -0.1936 | 0.576445 |
| 32 | -0.087963 | -0.6814 | 0.750868 |
| 33 | 0.115896 | 0.8977 | 0.18646 |
| 34 | -0.024886 | -0.1928 | 0.576103 |
| 35 | -0.02271 | -0.1759 | 0.56952 |
| 36 | -0.007845 | -0.0608 | 0.524128 |
| 37 | 0.094856 | 0.7348 | 0.232676 |
| 38 | -0.014842 | -0.115 | 0.545573 |
| 39 | -0.081893 | -0.6343 | 0.735865 |
| 40 | -0.008947 | -0.0693 | 0.52751 |
| 41 | -0.022841 | -0.1769 | 0.56992 |
| 42 | -0.14496 | -1.1229 | 0.867014 |
| 43 | -0.122754 | -0.9508 | 0.827251 |
| 44 | 0.144345 | 1.1181 | 0.133992 |
| 45 | -0.058878 | -0.4561 | 0.675006 |
| 46 | -0.032487 | -0.2516 | 0.598911 |
| 47 | 0.038139 | 0.2954 | 0.384346 |
| 48 | -0.034914 | -0.2704 | 0.606127 |
| 49 | 0.040001 | 0.3098 | 0.378876 |
| 50 | -0.02142 | -0.1659 | 0.565611 |
| 51 | -0.05874 | -0.455 | 0.674623 |
| 52 | -0.037638 | -0.2915 | 0.61418 |
| 53 | 0.002973 | 0.023 | 0.490853 |
| 54 | 0.01024 | 0.0793 | 0.468521 |
| 55 | 0.030047 | 0.2327 | 0.408377 |
| 56 | 0.033546 | 0.2598 | 0.397936 |
| 57 | -0.031746 | -0.2459 | 0.596703 |
| 58 | -0.044989 | -0.3485 | 0.63565 |
| 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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