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.

翻译：本文聚焦于近期提出的新型几何AR(1)（NoGeAR(1)）模型的相干预测——该模型是基于膨胀参数二项式稀释方法的INAR模型。现有多种技术可实现计数时间序列的h步超前相干预测，例如中位数预测和众数预测。然而，针对过度离散计数时间序列的相干预测研究文献仍显不足。本文采用蒙特卡洛（MC）近似方法研究NoGeAR(1)过程对应的预测分布。在仿真研究中，我们采用多种预测指标，以便对NoGeAR(1)与其他模型的预测能力进行全面比较。此外，本研究还通过真实数据——特别是CW{\ss} TeXpert下载数据和巴巴多斯COVID-19数据——验证了该方法的有效性。