Graph Spatio-Temporal Networks for Manufacturing Sales Forecast and Prevention Policies in Pandemic Era

Chia-Yen Lee; Shu-Huei Yang

doi:10.1016/j.cie.2023.109413

Graph Spatio-Temporal Networks for Manufacturing Sales Forecast and Prevention Policies in Pandemic Era

Comput Ind Eng. 2023 Jun 27:109413. doi: 10.1016/j.cie.2023.109413. Online ahead of print.

Authors

Chia-Yen Lee¹, Shu-Huei Yang²

Affiliations

¹ Department of Information Management, National Taiwan University, Taipei 106, Taiwan.
² Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan City 701, Taiwan.

Abstract

Worldwide manufacturing industries are significantly affected by COVID-19 pandemic because of their production characteristics with low-cost country sourcing, globalization, and inventory level. To analyze the correlated time series, spatial-temporal model becomes more attractive, and the graph convolution network (GCN) is also commonly used to provide more information to the nodes and its neighbors in the graph. Recently, attention-adjusted graph spatio-temporal network (AGSTN) was proposed to address the problem of pre-defined graph in GCN by combining multi-graph convolution and attention adjustment to learn spatial and temporal correlations over time. However, AGSTN may show potential problem with limited small non-sensor data; particularly, convergence issue. This study proposes several variants of AGSTN and applies them to non-sensor data. We suggest data augmentation and regularization techniques such as edge selection, time series decomposition, prevention policies to improve AGSTN. An empirical study of worldwide manufacturing industries in pandemic era was conducted to validate the proposed variants. The results show that the proposed variants significantly improve the prediction performance at least around 20% on mean squared error (MSE) and convergence problem.

Keywords: Correlated time series; Data augmentation; Data scarcity; Graph convolutional networks; Regularization.