# Fitting and Forecasting Gegenbauer ARMA Time Series Models

## Documentation for package ‘garma’ version 0.9.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

extract_arma | For a k=1 Gegenbauer process, transform to remove Gegenbauer long memory component to get a short memory (ARMA) process.
forecast.garma_model | The forecast function predicts future values of a "garma_model" object, and is exactly the same as the "predict" function with slightly different parameter values.
garma | garma: A package for estimating and foreasting Gegenbauer time series models.
garma_ggtsdisplay | For a k=1 Gegenbauer process, use semi-parametric methods to obtain short memory version of the process, then run a ggtsdisplay().
ggbr_semipara | For a k=1 Gegenbauer process, use semi-parametric methods to estimate the Gegenbauer frequency and fractional differencing.
ggplot.garma_model | The ggplot function generates a ggplot of actuals and predicted values for a "garma_model" object.
gg_raw_pgram | Display the raw periodogram for a time series, not on a log scale. The standard "R" functions display periodograms on a log scale which can make it more difficult to locate high peaks in the spectrum at differning frequencies. This routine will display the peaks on a raw scale.
plot.garma_model | The plot function generates a plot of actuals and predicted values for a "garma_model" object.
predict.garma_model | The predict function predicts future values of a "garma_model" object.
print.garma_model | The print function prints a summary of a "garma_model" object.
print.garma_semipara | Print a semiparameteric Gegenbauer estimation object.
print.ggbr_factors | Print a 'ggbr_factors' object.
summary.garma_model | The summary function provides a summary of a "garma_model" object.