A Novel Ensemble Learning Approach for Intelligent Logistics Demand Management

Logistics demand forecasting plays a crucial role in regulating logistics management activities, developing production plans, seeking maximum economic returns, and building smart logistics. Current studies have focused on forecasting logistics demand using various statistical algorithms and machine learning models. However, it is difficult for a single learner to forecast logistics demand time a novel ensemble learning approach (Deep Logistics Demand Forecasting, DeepLDF) is introduced in this work to forecast logistics demand. DeepLDF consists of two different base Model (MSTDCM) and the Seasonal Autoregressive logistics demand well.