Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2020

Abstract

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.

Keywords

Interactive visualization, model selection, product demand forecasting, time series

Discipline

Graphics and Human Computer Interfaces | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2020), Honolulu HI USA, April 25-30

First Page

1

Last Page

13

Identifier

10.1145/3313831.3376866

Publisher

ACM

City or Country

Virtual Conference

Additional URL

https://doi.org/10.1145/3313831.3376866

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