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：本文聚焦于对新近提出的新型几何AR(1)（NoGeAR(1)）模型进行相干预测——该模型是一种基于膨胀参数二项稀疏化方法的整数值自回归（INAR）模型。现有多种技术可用于实现计数时间序列的h步超前相干预测，例如中位数预测与众数预测。然而，针对过度离散计数时间序列的相干预测研究文献尚显不足。本文采用蒙特卡洛（MC）近似方法研究了NoGeAR(1)过程对应的预测分布。相应地，在模拟研究中运用了多种预测度量指标，以系统比较NoGeAR(1)模型与其他模型的预测能力。此外，通过实际数据（具体为CW{\ss} TeXpert下载数据与巴巴多斯COVID-19数据）对该方法进行了实证演示。