  Time series forecasting is essential for our daily activities and precise
modeling of the complex correlations and shared patterns among multiple time
series is essential for improving forecasting performance. Spatial-Temporal
Graph Neural Networks (STGNNs) are widely used in multivariate time series
forecasting tasks and have achieved promising performance on multiple
real-world datasets for their ability to model the underlying complex spatial
and temporal dependencies. However, existing studies have mainly focused on
datasets comprising only a few hundred sensors due to the heavy computational
cost and memory cost of spatial-temporal GNNs. When applied to larger datasets,
these methods fail to capture the underlying complex spatial dependencies and
exhibit limited scalability and performance. To this end, we present a Scalable
Adaptive Graph Diffusion Forecasting Network (SAGDFN) to capture complex
spatial-temporal correlation for large-scale multivariate time series and
thereby, leading to exceptional performance in multivariate time series
forecasting tasks. The proposed SAGDFN is scalable to datasets of thousands of
nodes without the need of prior knowledge of spatial correlation. Extensive
experiments demonstrate that SAGDFN achieves comparable performance with
state-of-the-art baselines on one real-world dataset of 207 nodes and
outperforms all state-of-the-art baselines by a significant margin on three
real-world datasets of 2000 nodes.
