LLM Reasoning Enhances Time Series Forecasting Ensemble Learning

Researchers have developed REATS, a novel ensemble learning framework for time series forecasting that integrates Large Language Model (LLM) reasoning. Unlike traditional methods that rely on numerical inputs or fixed rules, REATS uses LLMs to interpret textual descriptions of temporal patterns alongside numerical data, generating adaptive and interpretable ensemble weights. The system employs a structured input pipeline, a multi-row weight supervision scheme, and a two-stage fine-tuning process combining supervised fine-tuning (SFT) with GRPO to enhance performance and mitigate LLM hallucinations. Experiments across eight benchmarks show REATS outperforming existing ensemble methods and demonstrating strong generalization capabilities. AI

IMPACT Introduces a novel approach to leverage LLM reasoning for improved time series forecasting accuracy and interpretability.

RANK_REASON Academic paper detailing a new methodology for time series forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• GRPO
• Hugging Face
• LLM
• supervised fine-tuning

• paper
• model release

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