COROS: Context-Robust Time-Series Forecasting with Score-Based Diffusion Models

Diffusion models have emerged as an effective approach for probabilistic time-series forecasting, generating future trajectories from historical observations via iterative denoising. However, in many real-world applications the conditioning history may be contaminated by measurement noise or anomalies, which can degrade conditional generation. We propose COROS (COntext-RObust time-series forecasting with Score-based diffusion models), which extends score-based diffusion to the concatenated past–future sequence. By jointly denoising both the forecast horizon and the (possibly corrupted) history during sampling, COROS improves robust forecasting even under noisy conditions. Because noise is often introduced during observation, our method reduces sensitivity to corrupted historical data, thereby enhancing forecasting accuracy. Across five multivariate probabilistic forecasting benchmarks, COROS achieves competitive or best-in-class performance compared with 13 baselines. Furthermore, evaluation on standard LTSF benchmarks, where robustness to corrupted history is most transparently reflected in point-forecast degradation, demonstrates that COROS exhibits substantially smaller error growth compared to representative baselines as input noise increases.