Sales Forecasting in Cross Border E-Commerce using Convolutional Neural Network-Gated Recurrent Unit

Cross-border e-commerce has expanded quickly, precise sales forecasts are essential to maximizing marketing, logistics, and inventory control. Because commerce generates so much deal data every second, predictive analysis has never been more important for logistics management. To improve supply control and customer service, e-commerce companies are increasingly using AI technologies to improve forecasts. To improve the accuracy of sales forecasting, this study suggests a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) hybrid model. Where GRU records temporal dependencies and sequential patterns, CNN derives spatial dependencies from sales data. The model performs better, since it is trained using historical sales data, product features, and market trends from Amazon and Flipkart data. In the end, the suggested method optimizes business operations in cross-border e-commerce by improving demand forecasting and lowering instances of stockouts and overstock. With an accuracy score of 98.61%, precision score of 98.22%, recall score of 98.12%, and F1 score of 98.13%, the experimental results indicate that this implementation-based approach performs better. Enhancing system adaptability and using control-based techniques for increased sales prediction.