KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy

TL;DR

KITS introduces an incremental training strategy with virtual nodes and pseudo labels to improve inductive spatio-temporal kriging, effectively addressing the graph gap issue and outperforming existing methods significantly.

Contribution

The paper proposes a novel Increment training strategy with virtual nodes and pseudo labels to enhance generalization in inductive kriging models.

Findings

KITS outperforms existing methods with up to 18.33% MAE improvement.

The virtual node approach effectively mitigates the graph gap issue.

Experimental results validate the robustness and superiority of KITS.

Abstract

Sensors are commonly deployed to perceive the environment. However, due to the high cost, sensors are usually sparsely deployed. Kriging is the tailored task to infer the unobserved nodes (without sensors) using the observed source nodes (with sensors). The essence of kriging task is transferability. Recently, several inductive spatio-temporal kriging methods have been proposed based on graph neural networks, being trained based on a graph built on top of observed nodes via pretext tasks such as masking nodes out and reconstructing them. However, the graph in training is inevitably much sparser than the graph in inference that includes all the observed and unobserved nodes. The learned pattern cannot be well generalized for inference, denoted as graph gap. To address this issue, we first present a novel Increment training strategy: instead of masking nodes (and reconstructing them), we add virtual nodes into the training graph so as to mitigate the graph gap issue naturally. Nevertheless, the empty-shell virtual nodes without labels could have bad-learned features and lack supervision signals. To solve these issues, we pair each virtual node with its most similar observed node and fuse their features together; to enhance the supervision signal, we construct reliable pseudo labels for virtual nodes. As a result, the learned pattern of virtual nodes could be safely transferred to real unobserved nodes for reliable kriging. We name our new Kriging model with Increment Training Strategy as KITS. Extensive experiments demonstrate that KITS consistently outperforms existing kriging methods by large margins, e.g., the improvement over MAE score could be as high as 18.33%.