  This article focuses on the coherent forecasting of the recently introduced
novel geometric AR(1) (NoGeAR(1)) model - an INAR model based on inflated -
parameter binomial thinning approach. Various techniques are available to
achieve h - step ahead coherent forecasts of count time series, like median and
mode forecasting. However, there needs to be more body of literature addressing
coherent forecasting in the context of overdispersed count time series. Here,
we study the forecasting distribution corresponding to NoGeAR(1) process using
the Monte Carlo (MC) approximation method. Accordingly, several forecasting
measures are employed in the simulation study to facilitate a thorough
comparison of the forecasting capability of NoGeAR(1) with other models. The
methodology is also demonstrated using real-life data, specifically the data on
CW{\ss} TeXpert downloads and Barbados COVID-19 data.
