Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as meteorological prediction, urban computing. Adequate high-quality data, coupled with deep models capable of inference, are both indispensable and prerequisite for achieving meaningful results. However, the sparsity of data and the high costs associated with deploying sensors lead to significant data imbalances. Models that are overly tailored and lack causal relationships further compromise the generalizabilities of inference methods. Towards this end, we first establish a causal concept for ST predictions, named  NuwaDynamics, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Concretely, we initially leverage upstream self-supervision to discern causal important patches, imbuing the model with generalized information and conducting informed interventions on complementary trivial patches to extrapolate potential test distributions. This phase is referred to as the discovery step. Advancing beyond discovery step, we transfer the data to downstream tasks for targeted ST objectives, aiding the model in recognizing a broader potential distribution and fostering its causal perceptual capabilities (refer as Update step). Our concept aligns seamlessly with the contemporary backdoor adjustment mechanism in causality theory. Extensive experiments on six real-world ST benchmarks showcase that models can gain outcomes upon the integration of the NuwaDynamics concept. NuwaDynamics also can significantly benefit a wide range of changeable ST tasks like extreme weather and long temporal step super-resolution predictions.
NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling
Kun Wang Hao Wu Yifan Duan Guibin Zhang Kai Wang Xiaojiang Peng Yu Zheng Yuxuan Liang Yang Wang