Agentic Retrieval-Augmented Generation for Time Series Analysis

• URL: http://arxiv.org/abs/2408.14484v1
• Date: Sun, 18 Aug 2024 11:47:55 GMT
• Title: Agentic Retrieval-Augmented Generation for Time Series Analysis
• Authors: Chidaksh Ravuru, Sagar Srinivas Sakhinana, Venkataramana Runkana,
• Abstract summary: We propose a novel agentic Retrieval-Augmented Generation framework for time series analysis. Our proposed modular multi-agent RAG approach offers flexibility and achieves more state-of-the-art performance across major time series tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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