perARMA-package {perARMA}

Periodic Time Series Analysis and Modeling

Description

This package provides procedures for periodic time series analysis. The package includes procedures for nonparametric spectral analysis and parametric (PARMA) identification, estimation/fitting and prediction. The package is equipped with three examples of periodic time series: volumes and volumes.sep, which record hourly volumes of traded energy, and arosa containing monthly ozone levels.

Details

Package:	perARMA
Type:	Package
Version:	1.6
Date:	2016-02-25
License:	GPL(>=2.0)
LazyLoad:	yes

The main routines are:
Nonparametric spectral analysis: pgram, scoh
Preliminary identification and conditioning: permest, persigest
Identification: peracf, Bcoeff, perpacf, acfpacf
Parameter estimation/fitting: perYW, loglikec, parmaf, loglikef
Prediction: predictperYW, predseries
Simulation and testing: makeparma, parma_ident

For a complete list of procedures use library(help="perARMA").

Author(s)

Anna Dudek, Harry Hurd and Wioletta Wojtowicz
Maintainer: Karolina Marek <[e-mail]>

References

Hurd, H. L., Miamee, A. G., (2007), Periodically Correlated Random Sequences: Spectral Theory and Practice, Wiley InterScience.

See Also

Packages for Periodic Autoregression Analysis link{pear}, Dynamic Systems Estimation link{dse} and Bayesian and Likelihood Analysis of Dynamic Linear Models link{dlm}.

Examples

## Do not run
## It could take more than one minute
#demo(perARMA)