  Forecasting spatio-temporal correlated time series of sensor values is
crucial in urban applications, such as air pollution alert, biking resource
management, and intelligent transportation systems. While recent advances
exploit graph neural networks (GNN) to better learn spatial and temporal
dependencies between sensors, they cannot model time-evolving spatio-temporal
correlation (STC) between sensors, and require pre-defined graphs, which are
neither always available nor totally reliable, and target at only a specific
type of sensor data at one time. Moreover, since the form of time-series
fluctuation is varied across sensors, a model needs to learn fluctuation
modulation. To tackle these issues, in this work, we propose a novel GNN-based
model, Attention-adjusted Graph Spatio-Temporal Network (AGSTN). In AGSTN,
multi-graph convolution with sequential learning is developed to learn
time-evolving STC. Fluctuation modulation is realized by a proposed attention
adjustment mechanism. Experiments on three sensor data, air quality, bike
demand, and traffic flow, exhibit that AGSTN outperforms the state-of-the-art
methods.
