DIAMOND: Diffusion-Informed Patch Masking with Counterfactual Auditing for Time-Series Forecasting

Masking can improve time-series forecasting by reducing reliance on redundant historical information, but a fixed masking ratio may not suit every series, and masking prediction-relevant patches can harm accuracy. We propose DIAMOND, a training-time framework that adapts both the masking ratio and patch selection to each input series. A conditional diffusion branch estimates how readily historical patches can be reconstructed from visible context and provides evidence for a sample-specific masking budget. A scalar predictor estimates the masking ratio, while a direction predictor ranks candidate patches. To align patch selection with the forecasting task, DIAMOND periodically restores individual masked patches and measures the resulting change in prediction loss. This counterfactual feedback supervises patch ranking and helps protect patches that are important for forecasting. At inference, the trained forecaster uses the complete historical input without the masking controller or diffusion branch. Experiments across benchmark datasets and forecasting backbones demonstrate consistent improvements over existing approaches. Source code is available at https://anonymous.4open.science/r/Diamond-code-A812.