arXiv Machine Learning
TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models
Read the original on arXiv Machine Learning →TimeRSI is a self‑improving deployment system for time‑series foundation models that automates the creation and refinement of forecasting pipelines. A coding agent translates task descriptions into executable changes, tests each revision on historical data, and uses error feedback to guide further revisions. During live deployment, an adaptive selector chooses the best pipeline based on completed forecasts under similar conditions, achieving superior performance on 198 tasks compared to all other submissions.
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