The enhancement of predictive accuracy of the first-order integer autoregressive (INAR 1) model is the focus of the study through the effective amalgam of machine learning techniques and classical statistics. INAR (1) model structures are key elements for the prediction of nonnegative integer-valued time series processes. Conventional estimation methods include Classical Least Squares (CLS), Conditional Maximum Likelihood (CML), and Yule-Walker (YW) as compared with advanced methods like Support Vector Regression (SVR) and Genetic Algorithms (GA). A new clustering algorithm was created (with SVR to GA) to determine optimal INAR (1) model parameters. Computer simulation studies confirmed the effectiveness of the INAR-SVR and INAR-SVR-GA models compared to traditional methods.

The hybrid SVR-GA approach improved predictive accuracy by a wide margin, particularly with higher parameter values. This highlighted the robust performance of the SVR-GA-CML model, exhibiting uniform lending credibility at more diverse sample sizes and parameters. Integration of artificial intelligence into traditional statistical techniques in time series forecasting has taken the superior place. Enhancing accuracy is being turned into practical areas where forecasting economics and population studies need to make decision-making and allocation decisions with precise forecasts.

The originality of this study lies in the integration of statistical and AI hybrid techniques to upgrade the accuracy of time series forecasting. It provides the much-needed insight into the analysis of integer-valued time series and underscoring AI's transformational role in predictive analytics. Approval based on ethical standards was passed, and none of the contributors declared a conflict of interest.