Fitting and Forecasting Gegenbauer ARMA Time Series Models
AIC for model
ggplot of the Forecasts of the model.
Model Coefficients
Extract underlying ARMA process.
Return the fitted values for a GARMA forecast.
Extract fitted values
Forecast future values.
ggtsdisplay of underlying ARMA process.
garma: A package for estimating and foreasting Gegenbauer time series ...
Display raw periodogram
Extract semiparametric estimates of the Gegenbauer factors.
Goodness-of-Fit test for a garma_model.
Log Likelihood
Plot Forecasts from model.
Predict future values.
print a garma_model object.
Print a 'ggbr_factors' object.
Return the residuals for a GARMA forecast.
Residuals
summarise a garma_model object.
Diagnostic fit of a garma_model.
Covariance matrix
Methods for estimating univariate long memory-seasonal/cyclical Gegenbauer time series processes. See for example (2022) <doi:10.1007/s00362-022-01290-3>. Refer to the vignette for details of fitting these processes.