Adaptive Multi-Resolution Inference for Frozen Time-Series Foundation Models

Time-series foundation models (TSFMs) have demonstrated zero-shot forecasting capabilities, but standard inference typically queries the model only once at the original temporal resolution. This may leave predictive information at other time scales underused. We study how multiple temporal resolutions can improve forecasts at the original resolution when the foundation model is fully frozen and the number of queries is limited. To this end, we propose Adaptive Multi-Resolution Inference (AMRI). AMRI aggregates the observed history to multiple temporal resolutions and queries the same frozen TSFM at each resolution. The outputs predict different temporal aggregates of the same future trajectory and therefore share known structural relationships. Using these aggregation relationships and forecast reliability estimated from historical errors before testing, AMRI reconciles the outputs through closed-form weighted least squares to obtain the final forecast at the original resolution. Adaptivity comes from reliability calibration for the target series. The resolution set and calibrated weights remain fixed during testing. The entire procedure requires no gradient-based training, parameter updates, or additional learned forecasting modules. Under unbiased forecast errors and exact population inverse-covariance weighting, we provide an expected squared-error guarantee for the reconciled forecast relative to the original-resolution forecast. Experiments across multiple frozen TSFMs and forecasting benchmarks demonstrate the effectiveness of AMRI. Code is available in the anonymous repository.