Time Series Analysis and Random Forest Techniques for Prediction of Sales for Retail Grocery

Precise forecasting of retail food sales is crucial for inventory control, optimizing the supply chain, and meeting consumer demands. In this research work, we provide a strategy that combines the power of time series algorithms and random processes to achieve accurate sales prediction in the retail grocery store context. The challenges and complexities of forecasting sales in a dynamic, fast-paced retail setting are covered in this essay. We use a hybrid model that combines time series techniques with the advantages of random processes in order to overcome these difficulties.