When to Trust the Forecast: Post-hoc Failure Risk Estimation for Time Series Forecasting

Long-term time series forecasting requires more than low average error: in deployment, users often need to assess whether a generated trajectory is likely to contain large forecast errors. Standard evaluation protocols average errors over samples, variables, and horizons, thereby obscuring severe localized errors that may occur within an otherwise accurate forecast. We formulate post-hoc forecast failure risk estimation for long-term time series forecasting, where a fixed forecasting backbone, its historical input window, and its generated trajectory are used to estimate the risk of large forecast errors before the ground truth is observed. We propose \modelname, a backbone-agnostic model that addresses this problem by fusing historical dynamics, forecast behavior, and diagnostic mismatches between the history and forecast trajectory. Concretely, \modelname decomposes the history into trend and residual tokens, encodes the predicted trajectory with temporal-difference features, constructs diagnostic tokens from mismatch signals such as slope, volatility, range, jump, and lead-time position, and uses a Failure Decoder with Horizon Queries to produce fine-grained risk estimates along the forecast trajectory. By turning post-hoc forecast assessment from sequence-level scoring into fine-grained failure risk estimation along the forecast trajectory, \modelname provides actionable risk signals for long-term forecasting systems.