Spatio-Temporal Data Modelling and Monitoring  
Author Peihua Qiu




Abstract Spatio-temporal (ST) process monitoring has received a considerable attention recently due to its broad applications in environment monitoring, disease surveillance, streaming image processing, and more. Because ST data often have complicated structure, including latent ST data correlation, complex ST mean structure, and nonparametric data distribution, ST process monitoring is a challenging research problem. In the literature, there has been some discussion about ST data modeling and monitoring. But, the existing methods either impose various restrictive assumptions on the ST correlation that are hard to justify, or ignore partially or entirely the ST data correlation and ST data variation. In this talk, I will discuss some recent methodologies developed by my research group for ST data modeling and monitoring. Several real applications will also be used to demonstrate these methods.


    Article #:  DSBFI23-14
Proceedings of 2nd ISSAT International Conference on Data Science in Business, Finance and Industry
January 8-10, 2023 - Da Nang, Vietnam