Agentic Retrieval-Augmented Generation for Time Series Analysis
<|reference_start|>Agentic Retrieval-Augmented Generation for Time Series Analysis: Time series modeling is crucial for many applications, however, it faces challenges such as complex spatio-temporal dependencies and distribution shifts in learning from historical context to predict task-specific outcomes. To address these challenges, we propose a novel approach using an agentic Retrieval-Augmented Generation (RAG) framework for time series analysis. The framework leverages a hierarchical, multi-agent architecture where the master agent orchestrates specialized sub-agents and delegates the end-user request to the relevant sub-agent. The sub-agents utilize smaller, pre-trained language models (SLMs) customized for specific time series tasks through fine-tuning using instruction tuning and direct preference optimization, and retrieve relevant prompts from a shared repository of prompt pools containing distilled knowledge about historical patterns and trends to improve predictions on new data. Our proposed modular, multi-agent RAG approach offers flexibility and achieves state-of-the-art performance across major time series tasks by tackling complex challenges more effectively than task-specific customized methods across benchmark datasets.<|reference_end|>
@article{ravuru2024agentic,
    title={Agentic Retrieval-Augmented Generation for Time Series Analysis},
    author={Chidaksh Ravuru, Sagar Srinivas Sakhinana, Venkataramana Runkana},
    journal={arXiv preprint arXiv:2408.14484},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14484},
    primaryClass={cs.AI cs.CL cs.LG}
}