An Optimized Forecasting Approach for Virtual Trade Using a Hybrid Arima and Catboost Algorithm

The role of Sales forecasting for retail businesses is growing substantially, influencing inventory management practices and resource distribution and consumer demand forecasting. The ARIMA model stands as one of multiple traditional statistical tools used for time-series forecasting because it detects linear patterns together with temporal dependencies. These models face challenges while processing the nonlinear patterns which exist in sales data obtained from real-world scenarios. The CatBoost machine learning model demonstrates strong capabilities in processing nonlinear structures together with different variable types including categories however it cannot detect sequential interdependence. The research presents a new combined forecasting solution which unifies ARIMA and CatBoost so both modeling methodologies work together effectively to boost prediction reliability. The retail sales data analysis utilizes Root Mean Square Error (RMSE) as the performance measurement method. The proposed hybrid model yields an RMSE measure of 103,929.95 which proves better than single CatBoost performance (166,186.48) and standalone ARIMA results (116,704.24). The hybrid model shows a superior performance through its ability to handle features and time dependence which leads to better sales prediction results. Applying the hybrid model to real-world scenarios requires resolution of implementation complexity together with parameter optimization and minimal computational performance issues. The study demonstrates hybrid modeling systems within predictive analytics provide businesses with both extensive scalability and adaptability for situations characterized by uncertainty and dynamic changes. This work also highlights the multitenant hybrid model's flexibility and probability of using predictive analytics in various contexts.