| gsarima-package {gsarima} | R Documentation |
Two Functions for Generalized SARIMA Time Series Simulation
Description
Write SARIMA models in (finite) AR representation and simulate generalized multiplicative seasonal autoregressive moving average (time) series The methodology of this method is described in Briet OJT, Amerasinghe PH, and Vounatsou P (2013) <doi:10.1371/journal.pone.0065761>.
Details
| Package: | gsarima |
| Type: | Package |
| Version: | 0.1-5 |
| Date: | 2020-09-03 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
Use arrep() for converting the SARIMA function into AR representation, and use garsim() to simulate.
Author(s)
Olivier Briet <o.briet@gmail.com>
Maintainer: Olivier Briet <o.briet@gmail.com>
References
Briet OJT, Amerasinghe PH, Vounatsou P: Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers. PLoS ONE, 2013, 8(6): e65761. doi:10.1371/journal.pone.0065761 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0065761 If you use the gsarima package, please cite the above reference.
Brandt PT, Williams JT: A linear Poisson autoregressive model: The PAR(p). Political Analysis 2001, 9.
Benjamin MA, Rigby RA, Stasinopoulos DM: Generalized Autoregressive Moving Average Models. Journal of the American Statistical Association 2003, 98:214-223.
Zeger SL, Qaqish B: Markov regression models for time series: a quasi-likelihood approach. Biometrics 1988, 44:1019-1031
Grunwald G, Hyndman R, Tedesco L, Tweedie R: Non-Gaussian conditional linear AR(1) models. Australian & New Zealand Journal of Statistics 2000, 42:479-495.