DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting
This addresses the problem of model selection for demand analysts in manufacturing, offering a domain-specific tool that is incremental over existing methods.
The paper tackles the challenge of selecting demand forecasting models for manufacturing by introducing DFSeer, an interactive visualization system that compares models based on similar historical demand, revealing performance details and risks; case studies and expert interviews show its effectiveness and usability.
Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.