TimeRSI: Self-improving agentic deployment for time-series foundation models
The authors introduce TimeRSI, a self-improving agentic deployment system built around frozen time-series forecasting models: a coding agent translates task descriptions into executable pipeline changes, tests each revision on historical forecasting windows, and uses the observed errors to guide later revisions. At deployment time, an adaptive selector chooses among the resulting pipelines based on completed forecasts under similar conditions. The authors report that across 198 tasks on TIME and fev-bench, TimeRSI outperforms all submissions on six point and probabilistic forecasting metrics, and that controlled comparisons support feedback-guided revision over non-iterative pipeline search; code is available.
If reproducible, this style of self-improving pipeline could cut the manual tuning needed to deploy time-series forecasting.