More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting

Continual spatio-temporal forecasting supports traffic management and environmental monitoring under evolving dynamics and expanding sensor networks. However, conventional graph-based continual learning methods tie forecasting representations to the current sensor layout, so adding sensors can alter how previously learned spatial relationships are represented. Our key insight is that sensor expansion changes the evidence available about a process without necessarily changing the dynamics to be learned. Motivated by this insight, we propose STFO (Spatio-Temporal Field Operator), which parameterizes forecasting knowledge as a shared field-evolution operator while handling changing sensor layouts through observation and query interfaces. STFO lifts irregular sensor histories onto a fixed latent grid through normalized coordinate-based aggregation. This common computational domain enables reuse of learned spatial maps across observation sets without sensor-specific parameters. To accommodate process drift within this shared representation, a spectral descriptor summarizes variation across spatial scales and conditions Fourier propagation and attention, enabling the operator's responses to adjust to the current spatial regime. Coordinate-based decoding reads the evolved field at observed sensor locations and combines spatial corrections with local-history predictions. Experiments on PEMS-Stream, CA-Stream, and AIR-Stream demonstrate state-of-the-art average forecasting performance. Specifically, STFO -Large reduces average MAE relative to DOL by 8.4% on PEMS-Stream and 4.7% on CA-Stream. Our code is available at https://anonymous.4open.science/r/STFO2027/.