Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference

TL;DR

This paper introduces a novel approach for spatiotemporal inference that decouples long- and short-term pattern modeling, improving accuracy in environmental sensor data prediction by capturing distinct temporal relations.

Contribution

It proposes a joint graph attention network for short-term patterns and a graph recurrent network with time skip for long-term dependencies, addressing previous limitations in modeling temporal scales.

Findings

Achieves state-of-the-art results on four real-world datasets.

Effectively captures both long- and short-term spatiotemporal relations.

Outperforms existing methods in predictive accuracy.

Abstract

Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision-making. However, it is impractical to deploy massive sensors due to the expensive costs, resulting in sparse data collection. Therefore, how to get fine-grained data measurement has long been a pressing issue. In this paper, we aim to infer values at non-sensor locations based on observations from available sensors (termed spatiotemporal inference), where capturing spatiotemporal relationships among the data plays a critical role. Our investigations reveal two significant insights that have not been explored by previous works. Firstly, data exhibits distinct patterns at both long- and short-term temporal scales, which should be analyzed separately. Secondly, short-term patterns contain more delicate relations including those across spatial and temporal dimensions simultaneously, while long-term patterns involve high-level temporal trends. Based on these observations, we propose to decouple the modeling of short-term and long-term patterns. Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns. Furthermore, we propose a graph recurrent network with a time skip strategy to alleviate the gradient vanishing problem and model the long-term dependencies. Experimental results on four public real-world datasets demonstrate that our method effectively captures both long- and short-term relations, achieving state-of-the-art performance against existing methods.