  Spatiotemporal time series forecasting plays a key role in a wide range of
real-world applications. While significant progress has been made in this area,
fully capturing and leveraging spatiotemporal heterogeneity remains a
fundamental challenge. Therefore, we propose a novel Heterogeneity-Informed
Meta-Parameter Learning scheme. Specifically, our approach implicitly captures
spatiotemporal heterogeneity through learning spatial and temporal embeddings,
which can be viewed as a clustering process. Then, a novel spatiotemporal
meta-parameter learning paradigm is proposed to learn spatiotemporal-specific
parameters from meta-parameter pools, which is informed by the captured
heterogeneity. Based on these ideas, we develop a Heterogeneity-Informed
Spatiotemporal Meta-Network (HimNet) for spatiotemporal time series
forecasting. Extensive experiments on five widely-used benchmarks demonstrate
our method achieves state-of-the-art performance while exhibiting superior
interpretability. Our code is available at
https://github.com/XDZhelheim/HimNet.
