peracf, given an input time series and a specified period T, computes the periodic correlation coefficients such that
$\rho(t+\tau,t)=\rho(t,\tau)$, where $t = 1,\ldots, T$
are seasons and $\tau$ is lag. For each
possible pair of $t$ and $\tau$ confidence limits for
$\rho(t,\tau)$ are also computed using Fisher
transformation. Procedure peracf
provides also two important tests: $\rho(t+\tau,t) \equiv \rho(\tau)$ and $\rho(t+\tau,t) \equiv 0$.peracf(x, T, tau, missval, datastr,...)x will be treat as zeros and periodic mean will be computed,
then missing values will be replaced by periodic mean.prttaus, plottaus, cialpha, typeci, typerho, pchci, pchrho, colci, colrho, where
prttaus is a set of lags for which correlation coefficients are printed; it is a subset of peracf uses three separate procedures:
rhoci() returns the upper and lower bands defining a $1 - \alpha$ confidence interval for the true values of
$\rho(t, \tau)$,
rho.zero.test() tests whether the estimated correlation coefficients are equal to zeros, $\rho(t+\tau,t) \equiv 0$.
rho.equal.test() tests whether the estimated correlation coefficients are equal to each other for all seasons in the period,
$\rho(t+\tau,t) \equiv \rho(\tau)$.
In the test $\rho(t+\tau,t) \equiv \rho(\tau)$, rejection indicates
that series is properly PC and is not just an amplitude modulated stationary
sequence. That is, there exist lags for which $\rho(t+\tau,t)$
are properly periodic.
In the test $\rho(t+\tau,t) \equiv 0$, the
rejection for some $\tau \neq 0$ indicates the sequence is not PC white noise.Bcoeff, perpacfdata(volumes)
dev.set(which=1)
peracf(t(volumes),24,seq(1,12),NaN,'volumes')Run the code above in your browser using DataLab