Interpretable Spatial-Temporal Forecasting via Additive Neural Decomposition and Knowledge Distillation

Abstract

We challenge the prevailing assumption that interpretability requires sacrificing accuracy in spatial-temporal forecasting. We propose STGNAM (Spatial-Temporal Graph Neural Additive Model), which enforces a strict additive decomposition: 𝐲̂ = f_node + f_temporal + f_spatial + f_interact + bias, where each component is independently evaluable and visualizable. Our central finding is that the additive inductive bias acts as an implicit regularizer: on PEMS-BAY, STGNAM *sets a new state of the art* (MAE 1.62), surpassing all black-box models including TITAN (1.69) and its own D2STGNN teacher (1.87)—even without knowledge distillation (KD). When combined with KD from a black-box teacher, STGNAM achieves 91–104