Inducing stationarity in time series
| 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 | 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 8.4261 | 0 |
| 1 | -0.237896 | -2.0046 | 0.975587 |
| 2 | -0.125555 | -1.0579 | 0.853167 |
| 3 | 0.324345 | 2.733 | 0.003958 |
| 4 | -0.129082 | -1.0877 | 0.859789 |
| 5 | -0.182351 | -1.5365 | 0.935572 |
| 6 | -0.01375 | -0.1159 | 0.545954 |
| 7 | 0.039162 | 0.33 | 0.371191 |
| 8 | -0.073536 | -0.6196 | 0.731257 |
| 9 | 0.038805 | 0.327 | 0.372324 |
| 10 | -0.073893 | -0.6226 | 0.732241 |
| 11 | -0.077242 | -0.6508 | 0.741378 |
| 12 | -0.017991 | -0.1516 | 0.560031 |
| 13 | -0.130689 | -1.1012 | 0.862737 |
| 14 | -0.071438 | -0.6019 | 0.725437 |
| 15 | 0.153423 | 1.2928 | 0.100142 |
| 16 | -0.012366 | -0.1042 | 0.541346 |
| 17 | 0.043716 | 0.3684 | 0.356853 |
| 18 | 0.099797 | 0.8409 | 0.201613 |
| 19 | 0.102788 | 0.8661 | 0.194675 |
| 20 | -0.12243 | -1.0316 | 0.847123 |
| 21 | 0.043001 | 0.3623 | 0.359089 |
| 22 | 0.102252 | 0.8616 | 0.195906 |
| 23 | -0.119796 | -1.0094 | 0.8419 |
| 24 | 0.105065 | 0.8853 | 0.189494 |
| 25 | -0.007634 | -0.0643 | 0.525553 |
| 26 | -0.064072 | -0.5399 | 0.704516 |
| 27 | 0.048269 | 0.4067 | 0.342718 |
| 28 | -0.123859 | -1.0437 | 0.849907 |
| 29 | -0.124037 | -1.0452 | 0.850252 |
| 30 | 0.100824 | 0.8496 | 0.199214 |
| 31 | -0.008705 | -0.0734 | 0.529133 |
| 32 | -0.121404 | -1.023 | 0.845101 |
| 33 | 0.215977 | 1.8199 | 0.036498 |
| 34 | -0.062331 | -0.5252 | 0.699464 |
| 35 | -0.06251 | -0.5267 | 0.699984 |
| 36 | 0.106091 | 0.8939 | 0.187187 |
| 37 | -0.062867 | -0.5297 | 0.701023 |
| 38 | -0.003616 | -0.0305 | 0.512111 |
| 39 | -0.003795 | -0.032 | 0.512709 |
| 40 | 0.052287 | 0.4406 | 0.330429 |
| 41 | -0.000982 | -0.0083 | 0.50329 |
| 42 | 0.055099 | 0.4643 | 0.321936 |
| 43 | -0.057599 | -0.4853 | 0.685535 |
| 44 | -0.004687 | -0.0395 | 0.515697 |
| 45 | 0.051394 | 0.4331 | 0.333144 |
| 46 | -0.061304 | -0.5166 | 0.696466 |
| 47 | -0.061483 | -0.5181 | 0.696988 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.237896 | -2.0046 | 0.975587 |
| 1 | -0.193077 | -1.6269 | 0.945905 |
| 2 | 0.267306 | 2.2524 | 0.013696 |
| 3 | -0.005796 | -0.0488 | 0.519407 |
| 4 | -0.168237 | -1.4176 | 0.919657 |
| 5 | -0.232944 | -1.9628 | 0.973207 |
| 6 | -0.00509 | -0.0429 | 0.517044 |
| 7 | 0.032323 | 0.2724 | 0.393067 |
| 8 | 0.088824 | 0.7484 | 0.228333 |
| 9 | -0.172509 | -1.4536 | 0.924766 |
| 10 | -0.213755 | -1.8011 | 0.962036 |
| 11 | -0.168174 | -1.4171 | 0.919581 |
| 12 | -0.132319 | -1.1149 | 0.865682 |
| 13 | -0.133556 | -1.1254 | 0.867888 |
| 14 | 0.072671 | 0.6123 | 0.271134 |
| 15 | 0.00663 | 0.0559 | 0.477804 |
| 16 | 0.036895 | 0.3109 | 0.378401 |
| 17 | -0.075583 | -0.6369 | 0.736872 |
| 18 | 0.092169 | 0.7766 | 0.219979 |
| 19 | -0.095404 | -0.8039 | 0.787928 |
| 20 | 0.027246 | 0.2296 | 0.409539 |
| 21 | 0.04406 | 0.3713 | 0.355776 |
| 22 | 0.004136 | 0.0348 | 0.486149 |
| 23 | 0.052179 | 0.4397 | 0.330755 |
| 24 | -0.052771 | -0.4447 | 0.671038 |
| 25 | -0.085273 | -0.7185 | 0.762604 |
| 26 | 0.032898 | 0.2772 | 0.391213 |
| 27 | -0.05582 | -0.4703 | 0.680225 |
| 28 | -0.091068 | -0.7673 | 0.777291 |
| 29 | 0.030356 | 0.2558 | 0.39943 |
| 30 | 0.098526 | 0.8302 | 0.204605 |
| 31 | -0.03885 | -0.3274 | 0.627819 |
| 32 | 0.112867 | 0.951 | 0.172407 |
| 33 | -0.149902 | -1.2631 | 0.894656 |
| 34 | 0.01142 | 0.0962 | 0.461804 |
| 35 | 0.000793 | 0.0067 | 0.497343 |
| 36 | 0.021691 | 0.1828 | 0.427749 |
| 37 | -0.00233 | -0.0196 | 0.507804 |
| 38 | -0.118028 | -0.9945 | 0.838327 |
| 39 | -0.059493 | -0.5013 | 0.691144 |
| 40 | -0.023027 | -0.194 | 0.576646 |
| 41 | 0.096806 | 0.8157 | 0.208699 |
| 42 | -0.045594 | -0.3842 | 0.649005 |
| 43 | 0.006787 | 0.0572 | 0.477277 |
| 44 | 0.020477 | 0.1725 | 0.431752 |
| 45 | 0.031499 | 0.2654 | 0.39573 |
| 46 | -0.019805 | -0.1669 | 0.566031 |
| 47 | 0.004987 | 0.042 | 0.483301 |
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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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