Paper XIII acf pacf lag 60
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 4 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 7.6811 | 0 |
| 1 | 0.176852 | 1.3584 | 0.08975 |
| 2 | -0.156777 | -1.2042 | 0.883345 |
| 3 | -0.081949 | -0.6295 | 0.734262 |
| 4 | -0.106847 | -0.8207 | 0.792442 |
| 5 | 0.083429 | 0.6408 | 0.262057 |
| 6 | -0.058518 | -0.4495 | 0.672635 |
| 7 | -0.16866 | -1.2955 | 0.899903 |
| 8 | -0.201122 | -1.5448 | 0.936135 |
| 9 | -0.132206 | -1.0155 | 0.842991 |
| 10 | 0.040464 | 0.3108 | 0.37852 |
| 11 | 0.304832 | 2.3415 | 0.011303 |
| 12 | 0.079965 | 0.6142 | 0.270716 |
| 13 | -0.101222 | -0.7775 | 0.780016 |
| 14 | 0.095146 | 0.7308 | 0.233887 |
| 15 | -0.029426 | -0.226 | 0.58902 |
| 16 | 0.069462 | 0.5335 | 0.29783 |
| 17 | 0.055639 | 0.4274 | 0.335333 |
| 18 | -0.12609 | -0.9685 | 0.83163 |
| 19 | 0.013742 | 0.1056 | 0.458147 |
| 20 | 0.024908 | 0.1913 | 0.424464 |
| 21 | -0.019739 | -0.1516 | 0.559997 |
| 22 | -0.031368 | -0.2409 | 0.594782 |
| 23 | -0.063649 | -0.4889 | 0.686638 |
| 24 | 0.034697 | 0.2665 | 0.395387 |
| 25 | 0.118844 | 0.9129 | 0.182515 |
| 26 | -0.068796 | -0.5284 | 0.70041 |
| 27 | -0.065504 | -0.5031 | 0.691632 |
| 28 | -0.058003 | -0.4455 | 0.671217 |
| 29 | -0.056466 | -0.4337 | 0.666965 |
| 30 | 0.086232 | 0.6624 | 0.25516 |
| 31 | 0.02989 | 0.2296 | 0.409603 |
| 32 | -0.085983 | -0.6604 | 0.744232 |
| 33 | -0.134477 | -1.0329 | 0.847076 |
| 34 | -0.124111 | -0.9533 | 0.82784 |
| 35 | -0.003408 | -0.0262 | 0.510398 |
| 36 | 0.174141 | 1.3376 | 0.09308 |
| 37 | 0.093119 | 0.7153 | 0.238634 |
| 38 | 0.034273 | 0.2633 | 0.396634 |
| 39 | -0.011897 | -0.0914 | 0.536251 |
| 40 | -0.05404 | -0.4151 | 0.66021 |
| 41 | 0.190888 | 1.4662 | 0.073947 |
| 42 | 0.012205 | 0.0937 | 0.462813 |
| 43 | -0.176778 | -1.3579 | 0.91016 |
| 44 | -0.167754 | -1.2885 | 0.898707 |
| 45 | -0.130875 | -1.0053 | 0.840565 |
| 46 | -0.035033 | -0.2691 | 0.605603 |
| 47 | -0.010598 | -0.0814 | 0.532301 |
| 48 | 0.075254 | 0.578 | 0.28272 |
| 49 | 0.012148 | 0.0933 | 0.462986 |
| 50 | 0.005089 | 0.0391 | 0.484476 |
| 51 | 0.03745 | 0.2877 | 0.387308 |
| 52 | 0.152731 | 1.1732 | 0.122725 |
| 53 | 0.114177 | 0.877 | 0.19202 |
| 54 | -0.091742 | -0.7047 | 0.75811 |
| 55 | -0.029409 | -0.2259 | 0.588967 |
| 56 | 0.018804 | 0.1444 | 0.442826 |
| 57 | -0.049543 | -0.3805 | 0.647546 |
| 58 | -0.002389 | -0.0184 | 0.507289 |
| 59 | NA | NA | NA |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.176852 | 1.3584 | 0.08975 |
| 1 | -0.194125 | -1.4911 | 0.929369 |
| 2 | -0.015564 | -0.1195 | 0.547376 |
| 3 | -0.125489 | -0.9639 | 0.830486 |
| 4 | 0.121778 | 0.9354 | 0.176699 |
| 5 | -0.157376 | -1.2088 | 0.884224 |
| 6 | -0.105264 | -0.8085 | 0.788988 |
| 7 | -0.216715 | -1.6646 | 0.949355 |
| 8 | -0.100748 | -0.7739 | 0.778949 |
| 9 | -0.051065 | -0.3922 | 0.651852 |
| 10 | 0.27259 | 2.0938 | 0.020292 |
| 11 | -0.086252 | -0.6625 | 0.744889 |
| 12 | -0.013723 | -0.1054 | 0.541796 |
| 13 | 0.105612 | 0.8112 | 0.21025 |
| 14 | -0.110469 | -0.8485 | 0.800214 |
| 15 | 0.035829 | 0.2752 | 0.392059 |
| 16 | 0.019608 | 0.1506 | 0.440397 |
| 17 | -0.032844 | -0.2523 | 0.599149 |
| 18 | 0.128669 | 0.9883 | 0.163515 |
| 19 | 0.09072 | 0.6968 | 0.244323 |
| 20 | -0.037701 | -0.2896 | 0.613425 |
| 21 | -0.097311 | -0.7475 | 0.771122 |
| 22 | -0.014855 | -0.1141 | 0.545227 |
| 23 | 0.08268 | 0.6351 | 0.263916 |
| 24 | 0.043092 | 0.331 | 0.370909 |
| 25 | -0.075782 | -0.5821 | 0.718638 |
| 26 | 0.012907 | 0.0991 | 0.460682 |
| 27 | -0.136038 | -1.0449 | 0.849841 |
| 28 | 0.023248 | 0.1786 | 0.429444 |
| 29 | -0.036639 | -0.2814 | 0.610317 |
| 30 | -0.050383 | -0.387 | 0.649926 |
| 31 | -0.054865 | -0.4214 | 0.662513 |
| 32 | -0.103942 | -0.7984 | 0.786079 |
| 33 | -0.153864 | -1.1819 | 0.878997 |
| 34 | -0.120275 | -0.9238 | 0.820336 |
| 35 | 0.05326 | 0.4091 | 0.341975 |
| 36 | 0.069505 | 0.5339 | 0.297715 |
| 37 | 0.093748 | 0.7201 | 0.237157 |
| 38 | -0.020881 | -0.1604 | 0.563439 |
| 39 | -0.050704 | -0.3895 | 0.650833 |
| 40 | 0.101459 | 0.7793 | 0.219452 |
| 41 | -0.093425 | -0.7176 | 0.762086 |
| 42 | -0.086846 | -0.6671 | 0.746339 |
| 43 | -0.092293 | -0.7089 | 0.759416 |
| 44 | 0.065723 | 0.5048 | 0.307779 |
| 45 | -0.054391 | -0.4178 | 0.661191 |
| 46 | -0.055354 | -0.4252 | 0.663873 |
| 47 | 0.030355 | 0.2332 | 0.408221 |
| 48 | -0.071935 | -0.5525 | 0.708669 |
| 49 | -0.031066 | -0.2386 | 0.593889 |
| 50 | 0.012408 | 0.0953 | 0.462197 |
| 51 | 0.010494 | 0.0806 | 0.468015 |
| 52 | 0.0225 | 0.1728 | 0.431691 |
| 53 | -0.00457 | -0.0351 | 0.513941 |
| 54 | -0.03216 | -0.247 | 0.597126 |
| 55 | 0.071058 | 0.5458 | 0.293627 |
| 56 | -0.08559 | -0.6574 | 0.743269 |
| 57 | 0.094946 | 0.7293 | 0.234353 |
| 58 | NA | NA | NA |
| 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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