STREAM: Dynamic Channel Graphs with Autoregressive State Propagation for Multivariate Forecasting

Real-world multivariate series involve multiple dependencies: each chan- nel has its own dynamics, channels interact through spatial or physical structure, and these interactions can change across regimes. Yet standard benchmarks fix the channel set and lookback, allowing models that ignore inter-channel dependencies to remain competitive and leaving their value insufficiently tested. In deployment, sensors may fail, become noisy, or be added, while longer histories accumulate. We therefore evaluate dependency modeling through a controlled robustness protocol that expands the channel set along a sensor graph while scoring a fixed core set, and independently extends the lookback. We then present STREAM, which models channel dependency within each temporal patch using a sparse graph re-estimated from the patch, and models temporal dependency with a linear-time selec- tive state-space recurrence. Its bounded neighborhoods limit the effect of added channels, while the recurrence supports longer contexts with linear cost and patch-by-patch forecasting beyond the trained horizon.