autocorrelation
| 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 | 8.4853 | 0 |
| 1 | 0.940134 | 7.9773 | 0 |
| 2 | 0.898626 | 7.6251 | 0 |
| 3 | 0.866299 | 7.3508 | 0 |
| 4 | 0.806432 | 6.8428 | 0 |
| 5 | 0.747522 | 6.3429 | 0 |
| 6 | 0.710604 | 6.0297 | 0 |
| 7 | 0.673687 | 5.7164 | 0 |
| 8 | 0.623956 | 5.2944 | 1e-06 |
| 9 | 0.576903 | 4.8952 | 3e-06 |
| 10 | 0.527172 | 4.4732 | 1.4e-05 |
| 11 | 0.482031 | 4.0902 | 5.5e-05 |
| 12 | 0.431344 | 3.6601 | 0.000239 |
| 13 | 0.390793 | 3.316 | 0.000716 |
| 14 | 0.359422 | 3.0498 | 0.001602 |
| 15 | 0.340863 | 2.8923 | 0.002527 |
| 16 | 0.308536 | 2.618 | 0.005387 |
| 17 | 0.286344 | 2.4297 | 0.008803 |
| 18 | 0.259562 | 2.2025 | 0.015418 |
| 19 | 0.223601 | 1.8973 | 0.030898 |
| 20 | 0.188596 | 1.6003 | 0.056957 |
| 21 | 0.16277 | 1.3812 | 0.085752 |
| 22 | 0.122219 | 1.0371 | 0.151588 |
| 23 | 0.080712 | 0.6849 | 0.247815 |
| 24 | 0.056607 | 0.4803 | 0.316225 |
| 25 | 0.023323 | 0.1979 | 0.421838 |
| 26 | -0.00996 | -0.0845 | 0.53356 |
| 27 | -0.038655 | -0.328 | 0.628066 |
| 28 | -0.071939 | -0.6104 | 0.728247 |
| 29 | -0.104266 | -0.8847 | 0.810376 |
| 30 | -0.127414 | -1.0811 | 0.85838 |
| 31 | -0.159742 | -1.3555 | 0.910246 |
| 32 | -0.181934 | -1.5438 | 0.936485 |
| 33 | -0.194946 | -1.6542 | 0.948777 |
| 34 | -0.226318 | -1.9204 | 0.970614 |
| 35 | -0.242964 | -2.0616 | 0.978572 |
| 36 | -0.25502 | -2.1639 | 0.983105 |
| 37 | -0.276256 | -2.3441 | 0.989082 |
| 38 | -0.292902 | -2.4854 | 0.992367 |
| 39 | -0.301324 | -2.5568 | 0.993664 |
| 40 | -0.309747 | -2.6283 | 0.994759 |
| 41 | -0.322759 | -2.7387 | 0.996116 |
| 42 | -0.327548 | -2.7793 | 0.996528 |
| 43 | -0.336927 | -2.8589 | 0.997222 |
| 44 | -0.341716 | -2.8996 | 0.997524 |
| 45 | -0.356641 | -3.0262 | 0.998283 |
| 46 | -0.376155 | -3.1918 | 0.998952 |
| 47 | -0.39108 | -3.3184 | 0.999289 |
| 48 | -0.401415 | -3.4061 | 0.999459 |
| 49 | -0.42093 | -3.5717 | 0.999682 |
| 50 | -0.425719 | -3.6123 | 0.999721 |
| 51 | -0.415782 | -3.528 | 0.999633 |
| 52 | -0.414069 | -3.5135 | 0.999616 |
| 53 | -0.416946 | -3.5379 | 0.999645 |
| 54 | -0.402419 | -3.4146 | 0.999474 |
| 55 | -0.387893 | -3.2914 | 0.999227 |
| 56 | -0.387136 | -3.285 | 0.999211 |
| 57 | -0.359796 | -3.053 | 0.998413 |
| 58 | -0.341636 | -2.8989 | 0.99752 |
| 59 | -0.31793 | -2.6977 | 0.995655 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.940134 | 7.9773 | 0 |
| 1 | 0.127209 | 1.0794 | 0.142006 |
| 2 | 0.081791 | 0.694 | 0.244951 |
| 3 | -0.231581 | -1.965 | 0.973366 |
| 4 | -0.089049 | -0.7556 | 0.773824 |
| 5 | 0.127801 | 1.0844 | 0.140897 |
| 6 | 0.084737 | 0.719 | 0.23723 |
| 7 | -0.099284 | -0.8424 | 0.798837 |
| 8 | -0.119068 | -1.0103 | 0.842139 |
| 9 | -0.104965 | -0.8907 | 0.811959 |
| 10 | 0.080896 | 0.6864 | 0.247325 |
| 11 | -0.017525 | -0.1487 | 0.5589 |
| 12 | 0.039628 | 0.3363 | 0.36883 |
| 13 | 0.017983 | 0.1526 | 0.439574 |
| 14 | 0.136415 | 1.1575 | 0.125442 |
| 15 | -0.110698 | -0.9393 | 0.824641 |
| 16 | 0.009688 | 0.0822 | 0.467355 |
| 17 | -0.113067 | -0.9594 | 0.829717 |
| 18 | -0.027279 | -0.2315 | 0.591196 |
| 19 | -0.031108 | -0.264 | 0.603718 |
| 20 | 0.056308 | 0.4778 | 0.317124 |
| 21 | -0.175568 | -1.4897 | 0.92967 |
| 22 | -0.075853 | -0.6436 | 0.739072 |
| 23 | 0.048941 | 0.4153 | 0.339588 |
| 24 | 0.039658 | 0.3365 | 0.368732 |
| 25 | -0.008604 | -0.073 | 0.529 |
| 26 | -0.038597 | -0.3275 | 0.627882 |
| 27 | -0.117306 | -0.9954 | 0.838555 |
| 28 | 0.050846 | 0.4314 | 0.333717 |
| 29 | 0.051408 | 0.4362 | 0.331994 |
| 30 | -0.074954 | -0.636 | 0.736606 |
| 31 | -0.046245 | -0.3924 | 0.65204 |
| 32 | 0.053365 | 0.4528 | 0.326021 |
| 33 | -0.143601 | -1.2185 | 0.886493 |
| 34 | 0.075675 | 0.6421 | 0.261416 |
| 35 | -0.063932 | -0.5425 | 0.705417 |
| 36 | 0.004178 | 0.0354 | 0.48591 |
| 37 | 0.000876 | 0.0074 | 0.497044 |
| 38 | 0.020541 | 0.1743 | 0.431061 |
| 39 | 0.00321 | 0.0272 | 0.489174 |
| 40 | -0.060488 | -0.5133 | 0.69533 |
| 41 | -0.022045 | -0.1871 | 0.57393 |
| 42 | -0.018035 | -0.153 | 0.560599 |
| 43 | 0.065969 | 0.5598 | 0.288689 |
| 44 | -0.154189 | -1.3083 | 0.90254 |
| 45 | -0.146528 | -1.2433 | 0.89111 |
| 46 | -0.036312 | -0.3081 | 0.620559 |
| 47 | 0.077742 | 0.6597 | 0.255788 |
| 48 | 0.006341 | 0.0538 | 0.47862 |
| 49 | 0.019806 | 0.1681 | 0.433505 |
| 50 | 0.04361 | 0.37 | 0.35622 |
| 51 | 0.042318 | 0.3591 | 0.360293 |
| 52 | -0.075 | -0.6364 | 0.736731 |
| 53 | 0.045731 | 0.388 | 0.349565 |
| 54 | 0.054217 | 0.46 | 0.323435 |
| 55 | -0.026449 | -0.2244 | 0.58847 |
| 56 | 0.09045 | 0.7675 | 0.22265 |
| 57 | -0.094933 | -0.8055 | 0.788417 |
| 58 | 0.000502 | 0.0043 | 0.498307 |
| 59 | 0.074129 | 0.629 | 0.265669 |
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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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