MAFF: Mask-Aware Forecasting Framework for Time Series with Missing Values

Deployed time series are rarely complete: readings drop out, devices go offline, subsystems fail, and recent observations are lost before they are used. The standard remedy is to impute the gaps and forecast on the completed series, treating the repair as neutral. It is not. Imputed entries enter the instance-normalization statistics that modern forecasters depend on: interpolation deflates the window variance and shifts its mean, the model cannot distinguish fillers from measurements, and the resulting bias is applied back to the forecast at denormalization. We identify this contamination as a mechanistic, architecture-independent source of error, and we eliminate it. MAFF, our mask-aware forecasting framework, keeps observed entries as the only source of statistics, injects a gated summary of the missingness structure at an architecture-adapted position in the backbone, and substitutes a learnable token for fully-missing channels. The repair is parameter-free: it removes the contamination without adding capacity, and parameter-matched ablations isolate its contribution from the wrapper that carries it. Across four missingness protocols (random, block, correlated-group, and tail) at 30% missingness with masks shared across models, MAFF improves MSE on all five benchmarks when coupled with CMoS. We evaluate four backbone families independently: every one of them improves, and the gains are strongest and most consistent on CMoS, the convolution-based backbone whose statistics are most exposed to fillers.