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Time series forecasting is critical across domains like finance, energy, and meteorology, yet traditional models struggle with data sparsity, complex spatio-temporal dependencies, and generalization to unseen datasets. This paper proposes a novel framework that integrates Large Language Models (LLMs) with an agentic Retrieval-Augmented Generation (RAG) architecture to enhance forecasting capabilities. Our approach employs a hierarchical multi-agent system, dynamic retrieval with Transformer-based embeddings, and attention-based feature fusion to process time-series data, unstructured text, and external knowledge. The framework achieves superior accuracy, scalability, and zero-shot generalization by augmenting pre-trained LLMs with real-time context, addressing limitations in computational efficiency and cross-domain adaptability. Evaluated on benchmark datasets (e.g., M4, GluonTS, Weather), our model outperforms state-of-the-art baselines, including SARIMAX, XGBoost and LSTM. This work demonstrates the potential of agentic RAG to transform time series forecasting, offering a scalable, domain-agnostic solution for real-world applications.

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Enhancing Capabilities of Forecasting Models Using LLMs and Agentic RAG

  • 会议论文

  • 发表日期：

    2026年

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  • Basharat Hussain,
  • Akhtar Jamil,
  • Ahmad Raza Shaid,
  • Alaa Ali Hameed,
  • Saad B. Ahmed

摘要

Time series forecasting is critical across domains like finance, energy, and meteorology, yet traditional models struggle with data sparsity, complex spatio-temporal dependencies, and generalization to unseen datasets. This paper proposes a novel framework that integrates Large Language Models (LLMs) with an agentic Retrieval-Augmented Generation (RAG) architecture to enhance forecasting capabilities. Our approach employs a hierarchical multi-agent system, dynamic retrieval with Transformer-based embeddings, and attention-based feature fusion to process time-series data, unstructured text, and external knowledge. The framework achieves superior accuracy, scalability, and zero-shot generalization by augmenting pre-trained LLMs with real-time context, addressing limitations in computational efficiency and cross-domain adaptability. Evaluated on benchmark datasets (e.g., M4, GluonTS, Weather), our model outperforms state-of-the-art baselines, including SARIMAX, XGBoost and LSTM. This work demonstrates the potential of agentic RAG to transform time series forecasting, offering a scalable, domain-agnostic solution for real-world applications.