KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy

Qianxiong Xu, Cheng Long*, Ziyue Li*, Sijie Ruan, Rui Zhao, Zhishuai Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 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 inferior 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 methods by large margins, e.g., the improvement over MAE score could be as high as 18.33%.

Original languageEnglish
Title of host publicationSpecial Track on AI Alignment
EditorsToby Walsh, Julie Shah, Zico Kolter
PublisherAssociation for the Advancement of Artificial Intelligence
Pages12945-12953
Number of pages9
Edition12
ISBN (Electronic)157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 157735897X, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978, 9781577358978
DOIs
Publication statusPublished - 11 Apr 2025
Event39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States
Duration: 25 Feb 20254 Mar 2025

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number12
Volume39
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Country/TerritoryUnited States
CityPhiladelphia
Period25/02/254/03/25

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