Optimizing Retail Inventory and Sales through Advanced Time Series Forecasting using Fine Tuned PrGB Regressor

Retail transactions time series analysis helps the super market to analyze the customer behavior based on their purchases. It helps the super market to increase their sales based on the model components such as seasonality, impact of holidays and weather. This time series analysis helps the inventory management to analyze the trends, which in return helps the super market to get optimal stock and to have minimal loss. In existing approaches, time series analysis is performed using the pure statistical and anomaly detection approaches but statistical approaches assume the linear relation between the features due to which the model cannot extract the complex patterns. Without extracting the complex patterns, the parallel processing becomes difficult and also customer segmentation becomes difficult. In this research, the model fine tunes the prophet, gradient boost regressor and ensembles them to forecast the sales on daily basis. Boosting regressor can efficiently extract the relation between both internal and external factors to understand the complex patterns and trends on timely basis. Boosting algorithm has the capability to perform data analysis and feature engineering simultaneously. While capturing the patterns, the model needs long term analysis because of seasonal changes, festival offers, weather impacts and other parameters, these are analyzed by the prophet model along with shifts in the trends and component analysis. By combining XGBoost Regressor and Prophet for retail sales forecasting, model gain the ability to model both time-dependent patterns (seasonality, trends, holiday effects) and non-linear relationships (promotions, pricing, external factors), leading to improved forecast accuracy, robustness to real-world data challenges, and better decision-making capabilities in retail environments.