# TimesFM

Time-series foundation models forecast well zero-shot, yet deploying them still means hand-building input and output pipelines. TimeRSI has a coding agent repeatedly rewrite preprocessing, features, context selection and post-processing around a frozen forecaster, using errors on historical forecast windows to guide each revision, then picks among the resulting pipelines at deployment by similar past conditions. Across 198 tasks on TIME and FEV-bench it beats all published submissions on six point and probabilistic metrics; against direct inference with the same frozen TimesFM 3.0, TIME MASE falls 1.52% and CRPS 1.61%.

Time-series foundation models forecast well zero-shot, yet deploying them still means hand-building input and output pipelines. TimeRSI has a coding agent repeatedly rewrite preprocessing, features, context selection and post-processing around a frozen forecaster, using errors on historical forecast windows to guide each revision, then picks among the resulting pipelines at deployment by similar past conditions. Across 198 tasks on TIME and FEV-bench it beats all published submissions on six point and probabilistic metrics; against direct inference with the same frozen TimesFM 3.0, TIME MASE falls 1.52% and CRPS 1.61%.