perARMA-package: Periodic Time Series Analysis and Modeling

In perARMA: Periodic Time Series Analysis

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)

perARMA documentation built on Nov. 17, 2023, 9:06 a.m.