Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging

• URL: http://arxiv.org/abs/2109.12144v1
• Date: Fri, 24 Sep 2021 18:43:07 GMT
• Title: Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
• Authors: Yuankai Wu, Dingyi Zhuang, Mengying Lei, Aurelie Labbe, Lijun Sun
• Abstract summary: SATCN is a universal and flexible framework to performtemporal kriging for various datasets without need for model specification. We capture nodes by temporal convolutional networks, which allows our model to cope with data of diverse sizes. We conduct extensive experiments on three real-world datasets, including traffic and climate recordings.
• Score: 3.4386226615580107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatiotemporal kriging is an important application in spatiotemporal data analysis, aiming to recover/interpolate signals for unsampled/unobserved locations based on observed signals. The principle challenge for spatiotemporal kriging is how to effectively model and leverage the spatiotemporal dependencies within the data. Recently, graph neural networks (GNNs) have shown great promise for spatiotemporal kriging tasks. However, standard GNNs often require a carefully designed adjacency matrix and specific aggregation functions, which are inflexible for general applications/problems. To address this issue, we present 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. Specifically, we propose 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. To exclude information from unsampled nodes, a masking strategy that prevents the unsampled sensors from sending messages to their neighborhood is introduced to SAN. We capture temporal dependencies by the temporal convolutional networks, which allows our model to cope with data of diverse sizes. To make SATCN generalizable to unseen nodes and even unseen graph structures, we employ an inductive strategy to train SATCN. We conduct extensive experiments on three real-world spatiotemporal datasets, including traffic speed and climate recordings. Our results demonstrate the superiority of SATCN over traditional and GNN-based kriging models.

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