GenAutoML: An Agentic Framework for Dynamic Architecture Generation and Optimization in Time-Series Analysis
arXiv:2606.05860v1 Announce Type: new Abstract: Designing neural architectures for time-series forecasting and anomaly detection remains a resource-intensive task that often requires substantial domain expertise. Traditional Automated Machine Learning (AutoML) systems typically rely on static, predefined search spaces, limiting their ability to adapt to diverse data characteristics. We present GenAutoML, an agentic framework that leverages Large Language Models (LLMs) as neural architects to bridge natural-language requirements and executable PyTorch implementations. The framework incorporates
The rapid advancement of Large Language Models (LLMs) and the increasing demand for optimized machine learning solutions across diverse data types are converging, making agentic AutoML frameworks a timely development.
This development indicates a significant step towards more autonomous and efficient AI system design, potentially reducing the need for specialized human expertise in complex ML tasks, especially time-series analysis.
The reliance on static, predefined search spaces in AutoML systems is diminishing, replaced by dynamic, LLM-driven architecture generation that adapts to specific data characteristics and natural-language requirements.
- · AI researchers and developers
- · Companies with complex time-series data
- · Cloud AI platform providers
- · MLOps tool vendors
- · Traditional AutoML vendors with static architectures
- · Data scientists specializing only in static model design
- · Organizations slow to adopt agentic AI tools
GenAutoML allows for faster and more effective deployment of AI models for time-series forecasting and anomaly detection.
This efficiency will accelerate the adoption of predictive analytics in sectors like finance, manufacturing, and healthcare, identifying new patterns and optimizing operations.
The proliferation of such agentic systems could lead to a significant automation of the entire ML lifecycle, potentially redefining roles within data science and engineering teams.
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Read at arXiv cs.LG