Commodity Inventory Forecasting based on Label-Aware Transfer Learning

The competition between e-commerce platforms is becoming increasingly fierce, and inventory forecasting is important. The existing methods have problems such as low prediction accuracy and insufficient adaptation to market changes. This paper makes a time series model of commodity inventory and proposes a label-based transfer learning method for forecasting inventory. The model is oriented to establish a prediction process for different commodities and completes the prediction of inventory and sudden inventory in a continuous period. Experiments show that the prediction accuracy of the model is as high as 95.64 %.