seasonalMean, seasonalVar, seasonalACF {boodd} | R Documentation |
Computes time domain characteristics of periodically correlated time series
Description
Calculate estimates of the seasonal means, variances and autocovariances of a periodically correlated time series.
Usage
seasonalMean(x,period,...)
seasonalVar(x,period,...)
seasonalACF(x,tau,period,...)
## Default S3 method:
seasonalMean(x,period,...)
## Default S3 method:
seasonalVar(x,period,...)
## Default S3 method:
seasonalACF(x,tau,period,...)
## S3 method for class 'ts'
seasonalMean(x,period=frequency(x),...)
## S3 method for class 'ts'
seasonalVar(x,period=frequency(x),...)
## S3 method for class 'ts'
seasonalACF(x,tau,period=frequency(x),...)
Arguments
x |
A vector or time series representing a periodically correlated time series. |
period |
A positive integer; the period length. By default it is |
tau |
A vector of integers; a single lag or vector of lags. |
... |
Optional additional arguments for the function. |
Details
The functions seasonalMean
and seasonalVar
calculate
estimates of the seasonal means and variances respectively. The function
seasonalACF
calculates an estimator of the autocovariance for the given
lags.
Value
The seasonalMean
and seasonalVar
functions return a vector of length period
.
seasonalACF
returns either a vector of length period
if a single
lag tau
is specified, or a matrix with length(tau)
rows and period
columns if tau
is a vector.
References
Bertail, P. and Dudek, A. (2025). Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted..
Hurd, H.L., Miamee, A.G. (2007). Periodically Correlated Random Sequences: Spectral. Theory and Practice. Wiley.
See Also
blockboot.seasonal
,
meanCoeff
, acfCoeff
,
embb
,
embb.sample
,
bopt_circy
.
Examples
# Means
seasonalMean(nottem) # The period is already in the time-series object nottem
# Variances
seasonalVar(nottem)
# Autocovariances
seasonalACF(nottem,c(0,1))