[2603.04873] SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

Computer Science > Artificial Intelligence arXiv:2603.04873 (cs) [Submitted on 5 Mar 2026] Title:SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms Authors:Longkun Xu, Xiaochun Zhang, Qiantu Tuo, Rui Li View a PDF of the paper titled SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms, by Longkun Xu and 3 other authors View PDF HTML (experimental) Abstract:Accurate time series forecasting underpins decision-making across domains, yet conventional ML development suffers from data scarcity in new deployments, poor adaptability under distribution shift, and diminishing returns from manual iteration. We propose Self-Evolving Agent for Time Series Algorithms (SEA-TS), a framework that autonomously generates, validates, and optimizes forecasting code via an iterative self-evolution loop. Our framework introduces three key innovations: (1) Metric-Advantage Monte Carlo Tree Search (MA-MCTS), which replaces fixed rewards with a normalized advantage score for discriminative search guidance; (2) Code Review with running prompt refinement, where each executed solution undergoes automated review followed by prompt updates that encode corrective patterns, preventing recurrence of similar errors; and (3) Global Steerable Reasoning, which compares each node against global best and worst solutions, enabling cross-trajectory knowledge transfer. We adopt a MAP-Elites archive for architectural diversity...