Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging

McGill University, Feb. 2021 ~ Jun. 2021
Advisor: Prof. Lijun Sun
Co-worker: Yuankai Wu , Mengying Lei, Aurelie Labbe
Resources: ar5iv

• Presented SATCN–Spatial Aggregation and Temporal Convolution Networks–a universal and flexible framework to perform spatiotemporal kriging for various spatiotemporal datasets without the need for model specification.
• Proposed a novel spatial aggregation network (SAN) inspired by Principal Neighborhood Aggregation, which uses multiple aggregation functions to help one node gather diverse information from its neighbors
• Capture temporal dependencies by the temporal convolutional networks, which allows our model to cope with data of diverse sizes.
• Our results demonstrate the superiority of SATCN over traditional and GNN-based kriging models.