Better Dependencies, Better Forecasts: Dependency Rectification for Robust Time Series Forecasting

Time series forecasting typically assumes that predictive dependencies can be reliably learned from observed histories. However, real-world observations are often imperfect: noise, missing values, and localized distortions not only perturb individual values but also alter the apparent temporal dependencies that models rely on. As a result, directly fitting observed correlations may cause forecasting models to absorb spurious and non-generalizable relations, degrading robustness under corrupted inputs. To address this issue, we propose FenDiM, a Frequency-Enhanced Diffusion-Mamba forecasting architecture for prediction-oriented dependency rectification. FenDiM progressively suppresses corruption-induced dependencies through diffusion-based rectification, uses frequency-domain evidence to compensate for unreliable time-domain patterns, and leverages Mamba for efficient long-range temporal modeling. Across six benchmarks, FenDiM achieves the best horizon-averaged MSE on five datasets under standard observations, reducing MSE by 20.7% on average relative to the strongest competing baseline across these five datasets. At a prediction length of 192, it ranks within the top two in both MAE and MSE across all tested dataset–missing-rate settings and remains competitive under Gaussian noise. These results demonstrate the effectiveness of prediction-oriented dependency rectification for robust time series forecasting. The code and datasets are available at https://anonymous.4open.science/r/FenDim-5035.