REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

Recent advances in time series forecasting (TSF) have produced models with complementary design strengths, but no single model handles the diverse patterns of all samples. Existing ensembles rely on fixed rules or numerical-only black-box models, failing to exploit LLM reasoning or explain weighting decisions. We propose REATS, an intelligent LLM ensemble router that jointly processes textual temporal-pattern descriptions and numerical features to generate interpretable, sample-adaptive weights through chain-of-thought reasoning. To enable effective LLM-based ensembling, we study its key design choices and propose: (i) a structured input pipeline that converts raw series into hybrid textual–numerical representations with token cost fixed across input lengths, supports LLM reasoning and API-free rule-based chain-of-thought construction, and incorporates retrieved similar-sample priors; (ii) diverse multi-row weight supervision to enrich training signals, together with a token-efficient percentage-table format that reduces numerical complexity and LLM hallucinations; and (iii) a two-stage fine-tuning framework combining SFT for structured reasoning and GRPO with a reciprocal reward mapping that maps continuous, unbounded regression rewards to bounded signals, amplifies near-oracle sensitivity, and addresses uniform sensitivity and outlier-dominated advantage compression in mixed-quality rollout groups. Experiments on eight benchmarks show that REATS outperforms competitive ensemble baselines for both foundation-model and small-model candidate groups, while providing natural-language explanations and strong transfer learning and out-of-domain generalization to unseen candidate models.