Q2 Autocorrelation
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 3 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 | 0.224949 | 1.7424 | 0.043277 |
| 2 | -0.003033 | -0.0235 | 0.509332 |
| 3 | 0.369063 | 2.8587 | 0.00292 |
| 4 | 0.05152 | 0.3991 | 0.345628 |
| 5 | 0.178696 | 1.3842 | 0.085716 |
| 6 | 0.516584 | 4.0014 | 8.8e-05 |
| 7 | 0.122743 | 0.9508 | 0.17277 |
| 8 | 0.041713 | 0.3231 | 0.373869 |
| 9 | 0.228469 | 1.7697 | 0.040928 |
| 10 | -0.219192 | -1.6979 | 0.952642 |
| 11 | 0.033555 | 0.2599 | 0.39791 |
| 12 | 0.568115 | 4.4006 | 2.3e-05 |
| 13 | -0.019376 | -0.1501 | 0.559398 |
| 14 | -0.129444 | -1.0027 | 0.839975 |
| 15 | 0.124018 | 0.9606 | 0.170294 |
| 16 | -0.1507 | -1.1673 | 0.876149 |
| 17 | -0.011636 | -0.0901 | 0.535759 |
| 18 | 0.228095 | 1.7668 | 0.041172 |
| 19 | -0.041284 | -0.3198 | 0.624878 |
| 20 | -0.048612 | -0.3766 | 0.646082 |
| 21 | 0.064885 | 0.5026 | 0.308543 |
| 22 | -0.298111 | -2.3092 | 0.987803 |
| 23 | -0.005381 | -0.0417 | 0.516555 |
| 24 | 0.32846 | 2.5442 | 0.006772 |
| 25 | -0.081057 | -0.6279 | 0.733762 |
| 26 | -0.08571 | -0.6639 | 0.745352 |
| 27 | 0.008049 | 0.0623 | 0.475247 |
| 28 | -0.186932 | -1.448 | 0.923585 |
| 29 | -0.048605 | -0.3765 | 0.646062 |
| 30 | 0.084613 | 0.6554 | 0.257356 |
| 31 | -0.07443 | -0.5765 | 0.716793 |
| 32 | -0.044952 | -0.3482 | 0.635543 |
| 33 | -0.08958 | -0.6939 | 0.754783 |
| 34 | -0.277354 | -2.1484 | 0.982135 |
| 35 | -0.084911 | -0.6577 | 0.743383 |
| 36 | 0.077457 | 0.6 | 0.27539 |
| 37 | -0.132918 | -1.0296 | 0.84633 |
| 38 | -0.179468 | -1.3901 | 0.91519 |
| 39 | -0.136939 | -1.0607 | 0.853467 |
| 40 | -0.208262 | -1.6132 | 0.944024 |
| 41 | -0.146784 | -1.137 | 0.869967 |
| 42 | -0.07782 | -0.6028 | 0.725542 |
| 43 | -0.121761 | -0.9432 | 0.825307 |
| 44 | -0.106934 | -0.8283 | 0.794611 |
| 45 | -0.151503 | -1.1735 | 0.877392 |
| 46 | -0.218727 | -1.6943 | 0.952299 |
| 47 | -0.113864 | -0.882 | 0.809348 |
| 48 | -0.026906 | -0.2084 | 0.582193 |
| 49 | -0.055268 | -0.4281 | 0.664946 |
| 50 | -0.06263 | -0.4851 | 0.685324 |
| 51 | -0.045463 | -0.3522 | 0.637021 |
| 52 | -0.054054 | -0.4187 | 0.661534 |
| 53 | -0.029497 | -0.2285 | 0.589977 |
| 54 | -0.014409 | -0.1116 | 0.544248 |
| 55 | 0.009026 | 0.0699 | 0.472245 |
| 56 | 0.026879 | 0.2082 | 0.417887 |
| 57 | 1e-06 | 0 | 0.499998 |
| 58 | -0.00315 | -0.0244 | 0.509694 |
| 59 | -0.000236 | -0.0018 | 0.500727 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.224949 | 1.7424 | 0.043277 |
| 1 | -0.056494 | -0.4376 | 0.668375 |
| 2 | 0.404168 | 3.1307 | 0.001347 |
| 3 | -0.171762 | -1.3305 | 0.905799 |
| 4 | 0.348336 | 2.6982 | 0.00452 |
| 5 | 0.290769 | 2.2523 | 0.013988 |
| 6 | 0.008392 | 0.065 | 0.474192 |
| 7 | -0.00972 | -0.0753 | 0.529882 |
| 8 | -0.037139 | -0.2877 | 0.612708 |
| 9 | -0.444147 | -3.4403 | 0.999468 |
| 10 | 0.120242 | 0.9314 | 0.177692 |
| 11 | 0.38554 | 2.9864 | 0.002041 |
| 12 | -0.171027 | -1.3248 | 0.904864 |
| 13 | -0.107907 | -0.8358 | 0.796719 |
| 14 | -0.086541 | -0.6703 | 0.747394 |
| 15 | 0.107447 | 0.8323 | 0.204275 |
| 16 | -0.061889 | -0.4794 | 0.683297 |
| 17 | -0.127533 | -0.9879 | 0.836408 |
| 18 | 0.086156 | 0.6674 | 0.253551 |
| 19 | -0.042827 | -0.3317 | 0.629379 |
| 20 | 0.030061 | 0.2329 | 0.408335 |
| 21 | -0.008918 | -0.0691 | 0.527421 |
| 22 | 0.16224 | 1.2567 | 0.106866 |
| 23 | -0.073937 | -0.5727 | 0.71551 |
| 24 | 0.042437 | 0.3287 | 0.371757 |
| 25 | 0.004327 | 0.0335 | 0.486687 |
| 26 | -0.116881 | -0.9054 | 0.815551 |
| 27 | -0.074381 | -0.5762 | 0.716666 |
| 28 | -0.0782 | -0.6057 | 0.726512 |
| 29 | -0.042066 | -0.3258 | 0.62716 |
| 30 | -0.006703 | -0.0519 | 0.520619 |
| 31 | -0.038502 | -0.2982 | 0.616724 |
| 32 | -0.087747 | -0.6797 | 0.750341 |
| 33 | 0.158365 | 1.2267 | 0.112365 |
| 34 | -0.186255 | -1.4427 | 0.922851 |
| 35 | 0.010099 | 0.0782 | 0.468955 |
| 36 | -0.065496 | -0.5073 | 0.693108 |
| 37 | -0.167046 | -1.2939 | 0.899677 |
| 38 | 0.048514 | 0.3758 | 0.354201 |
| 39 | -0.031895 | -0.2471 | 0.597145 |
| 40 | 0.041568 | 0.322 | 0.374292 |
| 41 | -0.066688 | -0.5166 | 0.696321 |
| 42 | -0.001401 | -0.0109 | 0.504311 |
| 43 | 0.046151 | 0.3575 | 0.360991 |
| 44 | 0.065725 | 0.5091 | 0.306272 |
| 45 | -0.076861 | -0.5954 | 0.723079 |
| 46 | -0.041402 | -0.3207 | 0.625224 |
| 47 | -0.061997 | -0.4802 | 0.683594 |
| 48 | 0.05458 | 0.4228 | 0.336985 |
| 49 | 0.208606 | 1.6159 | 0.055686 |
| 50 | 0.028858 | 0.2235 | 0.411939 |
| 51 | 0.02557 | 0.1981 | 0.421832 |
| 52 | 0.015729 | 0.1218 | 0.451717 |
| 53 | 0.021759 | 0.1685 | 0.433361 |
| 54 | -0.143939 | -1.115 | 0.865341 |
| 55 | 0.001406 | 0.0109 | 0.495673 |
| 56 | -0.036784 | -0.2849 | 0.611659 |
| 57 | 0.000609 | 0.0047 | 0.498125 |
| 58 | 0.059664 | 0.4622 | 0.322822 |
| 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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