Practical Solutions for Time Series Analysis
Enhancing Time Series Analysis with Agentic-RAG Framework
Time series modeling is crucial for various applications such as demand planning and anomaly detection. However, it faces challenges like high dimensionality and distribution shifts. Traditional methods rely on specific neural network designs, but there is potential in adapting small-scale pretrained language models (SLMs) for universal time series applications.
The proposed Agentic-RAG framework introduces a hierarchical, multi-agent architecture for time series analysis. It employs specialized sub-agents fine-tuned with SLMs for tasks like forecasting and anomaly detection. These sub-agents retrieve relevant prompts from knowledge repositories, enabling better predictions on new data.
The framework utilizes a dynamic prompting mechanism to adapt to various trends within complex time series data, overcoming the limitations of traditional fixed-window methods. It also incorporates advanced attention mechanisms and fine-tuning techniques to enhance SLM performance on specific time series tasks.
When evaluated across forecasting, classification, anomaly detection, and imputation tasks, the framework consistently outperformed baselines, demonstrating superior predictive accuracy and robustness.
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Agentic-RAG offers a hierarchical multi-agent framework for enhanced time series analysis, providing practical solutions for addressing distribution shifts and fixed-length subsequences. It enables flexibility and achieves state-of-the-art performance across major time series benchmarks.
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