arXiv:2408.14484v1 Announce Type: new
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.
Time series modeling plays a critical role in numerous applications, but it encounters various challenges such as intricate spatio-temporal dependencies and distribution shifts when learning from historical context to predict task-specific outcomes. In light of these challenges, a groundbreaking approach, the agentic Retrieval-Augmented Generation (RAG) framework, has been proposed for time series analysis.
The innovative framework takes advantage of a hierarchical, multi-agent architecture in which a master agent coordinates specialized sub-agents and delegates the end-user request to the appropriate sub-agent. These sub-agents employ smaller, pre-trained language models (SLMs) that are tailored for specific time series tasks through fine-tuning using instruction tuning and direct preference optimization. Additionally, they retrieve pertinent prompts from a shared repository of prompt pools that contain distilled knowledge about historical patterns and trends, thereby enhancing predictions on new data.
One of the notable aspects of the proposed modular, multi-agent RAG approach is its flexibility. By addressing complex challenges more effectively than task-specific customized methods across benchmark datasets, this approach achieves state-of-the-art performance across major time series tasks. It recognizes the multi-disciplinary nature of time series analysis, incorporating techniques from natural language processing, machine learning, and data retrieval. This multi-disciplinary approach allows for a more comprehensive understanding of time series data and facilitates more accurate predictions.
The integration of language models and retrieval methods in the RAG framework paves the way for significant advancements in time series modeling. By leveraging pre-existing knowledge and distilling it into prompts, the framework removes the burden of learning complex dependencies solely from historical data. The utilization of sub-agents with specialized models enables a more efficient and targeted analysis of different aspects of the time series tasks.
Looking ahead, the multi-disciplinary nature of the RAG framework opens up exciting possibilities for further research and development. The integration of additional data sources, such as external environmental factors, could enhance the accuracy of predictions even further. Additionally, exploring alternative fine-tuning methods and knowledge distillation techniques may uncover new strategies for optimizing the performance of the sub-agents.
In conclusion, the proposed agentic Retrieval-Augmented Generation (RAG) framework offers a novel and powerful approach to time series analysis. By combining multi-agent architecture, specialized language models, and retrieval-based knowledge augmentation, this framework addresses the challenges inherent in time series modeling and achieves state-of-the-art performance. Its multi-disciplinary nature and modular design make it a versatile and adaptable solution, poised to drive advancements in the field.