SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms
Accurate time series forecasting underpins decision-making in many domains, yetconventional ML development often faces data scarcity, distribution shift, anddiminishing returns from manual iteration. We propose Self-Evolving Agent forTime Series Algorithms (SEATS), a framework that autonomously generates, val-idates, and optimizes forecasting algorithm code through an iterative self-evolutionloop. Our design combines three mechanisms: (1) Metric-Advantage MCTS(MA-MCTS), which replaces fixed rewards with a statistically normalized advan-tage score for search guidance, (2) code review with running prompt refinement,so every successfully executed solution is reviewed and the running prompt e...
| Authors | Longkun Xu, Xiaochun Zhang, Qiantu Tuo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 March 2026 |
| Last updated at source | 26 June 2026 |
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