Commodity demand forecasting based on multimodal data and recurrent neural networks for E-commerce platforms

The study proposes a cascaded hybrid neural network commodity demand prediction model based on multimodal data. This model aims to improve the accuracy of commodity demand forecasts on e-commerce platforms. By constructing multimodal data feature clusters and utilizing a spatial feature fusion strategy, historical order information, and product evaluation sentiment data are integrated. The model combines the advantages of bi-directional long and short-term memory networks and bi-directional gated recurrent unit networks. The proposed cascaded hybrid strategy-based model significantly enhances accuracy in commodity demand forecasting. Results indicated an average absolute error of 0.1682 and root mean square error of 0.4537 for weekly commodity forecasts. For long-term commodity demand, the average absolute error was 0.8611 with a root mean square error of 8.1938. These outcomes highlight the algorithm's high prediction accuracy, making it valuable for commodity demand prediction on e-commerce platforms and providing a framework for effective inventory management.