  In this article, we introduce a Gegenbauer autoregressive tempered
fractionally integrated moving average (GARTFIMA) process. We work on the
spectral density and autocovariance function for the introduced process. The
parameter estimation is done using the empirical spectral density with the help
of the nonlinear least square technique and the Whittle likelihood estimation
technique. The performance of the proposed estimation techniques is assessed on
simulated data. Further, the introduced process is shown to better model the
real-world data in comparison to other time series models.
