Inducing Stationarity in Time Series-Q2c
| 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 | 7.6811 | 0 |
| 1 | -0.122329 | -0.9396 | 0.824379 |
| 2 | -0.276956 | -2.1273 | 0.981209 |
| 3 | -0.022947 | -0.1763 | 0.569652 |
| 4 | -0.073263 | -0.5627 | 0.712129 |
| 5 | 0.197104 | 1.514 | 0.067684 |
| 6 | 0.026055 | 0.2001 | 0.421032 |
| 7 | -0.204917 | -1.574 | 0.939582 |
| 8 | 0.036067 | 0.277 | 0.39136 |
| 9 | -0.065135 | -0.5003 | 0.690641 |
| 10 | 0.285523 | 2.1931 | 0.016125 |
| 11 | 0.231353 | 1.7771 | 0.040357 |
| 12 | -0.477995 | -3.6716 | 0.99974 |
| 13 | -0.104672 | -0.804 | 0.787688 |
| 14 | 0.20954 | 1.6095 | 0.056422 |
| 15 | 0.004476 | 0.0344 | 0.486344 |
| 16 | 0.108254 | 0.8315 | 0.204518 |
| 17 | -0.238436 | -1.8315 | 0.963959 |
| 18 | -0.069565 | -0.5343 | 0.702441 |
| 19 | 0.114201 | 0.8772 | 0.191969 |
| 20 | 0.061611 | 0.4732 | 0.318894 |
| 21 | 0.068741 | 0.528 | 0.299736 |
| 22 | -0.073799 | -0.5669 | 0.713521 |
| 23 | -0.174579 | -1.341 | 0.907464 |
| 24 | 0.080397 | 0.6175 | 0.269626 |
| 25 | 0.096895 | 0.7443 | 0.229835 |
| 26 | -0.086644 | -0.6655 | 0.745847 |
| 27 | -0.041585 | -0.3194 | 0.624733 |
| 28 | -0.065895 | -0.5062 | 0.692682 |
| 29 | 0.047527 | 0.3651 | 0.358186 |
| 30 | 0.074638 | 0.5733 | 0.284308 |
| 31 | -0.006519 | -0.0501 | 0.519884 |
| 32 | 0.015215 | 0.1169 | 0.453681 |
| 33 | -0.053625 | -0.4119 | 0.659048 |
| 34 | 0.000682 | 0.0052 | 0.497918 |
| 35 | 0.014377 | 0.1104 | 0.45622 |
| 36 | 0.020208 | 0.1552 | 0.438588 |
| 37 | -0.120812 | -0.928 | 0.821399 |
| 38 | 0.032478 | 0.2495 | 0.401934 |
| 39 | -0.001156 | -0.0089 | 0.503526 |
| 40 | -0.009391 | -0.0721 | 0.528631 |
| 41 | 0.161645 | 1.2416 | 0.109645 |
| 42 | 0.03116 | 0.2393 | 0.405834 |
| 43 | -0.091451 | -0.7024 | 0.757418 |
| 44 | 0.000521 | 0.004 | 0.498409 |
| 45 | -0.050612 | -0.3888 | 0.650572 |
| 46 | -9.5e-05 | -7e-04 | 0.50029 |
| 47 | -0.037431 | -0.2875 | 0.612635 |
| 48 | 0.033985 | 0.261 | 0.397485 |
| 49 | 0.019857 | 0.1525 | 0.439647 |
| 50 | 0.001684 | 0.0129 | 0.494862 |
| 51 | -0.011486 | -0.0882 | 0.535003 |
| 52 | 0.068383 | 0.5253 | 0.300685 |
| 53 | -0.108621 | -0.8343 | 0.79627 |
| 54 | -0.03108 | -0.2387 | 0.593927 |
| 55 | 0.024085 | 0.185 | 0.426932 |
| 56 | 0.038425 | 0.2951 | 0.384458 |
| 57 | -0.008393 | -0.0645 | 0.525591 |
| 58 | 0.024299 | 0.1866 | 0.426289 |
| 59 | NA | NA | NA |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.122329 | -0.9396 | 0.824379 |
| 1 | -0.296355 | -2.2763 | 0.986765 |
| 2 | -0.114765 | -0.8815 | 0.809196 |
| 3 | -0.201693 | -1.5492 | 0.936664 |
| 4 | 0.12392 | 0.9518 | 0.172529 |
| 5 | 0.001918 | 0.0147 | 0.494148 |
| 6 | -0.127041 | -0.9758 | 0.833431 |
| 7 | -0.002913 | -0.0224 | 0.508888 |
| 8 | -0.143837 | -1.1048 | 0.863142 |
| 9 | 0.27316 | 2.0982 | 0.02009 |
| 10 | 0.291854 | 2.2418 | 0.014375 |
| 11 | -0.259259 | -1.9914 | 0.974464 |
| 12 | -0.118298 | -0.9087 | 0.816387 |
| 13 | 0.080087 | 0.6152 | 0.270408 |
| 14 | -0.06246 | -0.4798 | 0.683416 |
| 15 | 0.028548 | 0.2193 | 0.413594 |
| 16 | -0.140086 | -1.076 | 0.85685 |
| 17 | -0.032118 | -0.2467 | 0.597003 |
| 18 | -0.105145 | -0.8076 | 0.788728 |
| 19 | 0.021386 | 0.1643 | 0.435042 |
| 20 | -0.08677 | -0.6665 | 0.746153 |
| 21 | 0.105667 | 0.8116 | 0.210129 |
| 22 | 0.127141 | 0.9766 | 0.16638 |
| 23 | -0.183444 | -1.4091 | 0.917967 |
| 24 | -0.056598 | -0.4347 | 0.667331 |
| 25 | -0.068366 | -0.5251 | 0.699268 |
| 26 | 0.00444 | 0.0341 | 0.486453 |
| 27 | 0.018961 | 0.1456 | 0.442351 |
| 28 | -0.084633 | -0.6501 | 0.740918 |
| 29 | -0.119679 | -0.9193 | 0.819152 |
| 30 | 0.011774 | 0.0904 | 0.464124 |
| 31 | 0.071062 | 0.5458 | 0.293618 |
| 32 | -0.016988 | -0.1305 | 0.551687 |
| 33 | 0.068548 | 0.5265 | 0.300247 |
| 34 | -0.015568 | -0.1196 | 0.54739 |
| 35 | -0.048648 | -0.3737 | 0.645007 |
| 36 | -0.143425 | -1.1017 | 0.86246 |
| 37 | -0.003179 | -0.0244 | 0.509699 |
| 38 | -0.068457 | -0.5258 | 0.699511 |
| 39 | -0.018877 | -0.145 | 0.557398 |
| 40 | 0.140644 | 1.0803 | 0.142201 |
| 41 | 0.039998 | 0.3072 | 0.379876 |
| 42 | -0.057661 | -0.4429 | 0.670272 |
| 43 | 0.070033 | 0.5379 | 0.296322 |
| 44 | -0.038546 | -0.2961 | 0.615896 |
| 45 | -0.050108 | -0.3849 | 0.649147 |
| 46 | -0.109381 | -0.8402 | 0.797897 |
| 47 | 0.0767 | 0.5891 | 0.279006 |
| 48 | -0.09123 | -0.7008 | 0.756894 |
| 49 | 0.014724 | 0.1131 | 0.455168 |
| 50 | -0.095708 | -0.7351 | 0.767419 |
| 51 | -0.172113 | -1.322 | 0.904369 |
| 52 | 0.027069 | 0.2079 | 0.418003 |
| 53 | 0.006669 | 0.0512 | 0.479658 |
| 54 | -0.075045 | -0.5764 | 0.716742 |
| 55 | 0.079584 | 0.6113 | 0.271675 |
| 56 | 0.00574 | 0.0441 | 0.482491 |
| 57 | 0.032046 | 0.2461 | 0.403211 |
| 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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