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[2602.01445] A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting
Summary
The paper introduces LLM-AutoOpt, a novel framework that enhances hyperparameter optimization in time-series forecasting by integrating large language models with Bayesian optimization.
Why It Matters
This research addresses the challenges of hyperparameter optimization, which is crucial for improving model performance in time-series forecasting. By leveraging LLMs for contextual reasoning, the framework promises to enhance interpretability and efficiency in optimization processes, making it relevant for data scientists and machine learning practitioners.
Key Takeaways
- LLM-AutoOpt combines Bayesian Optimization with LLMs for better hyperparameter tuning.
- The framework improves predictive performance in time-series forecasting tasks.
- It enhances interpretability by exposing the reasoning behind optimization decisions.
- Incorporates structured meta-knowledge to mitigate cold-start effects.
- Demonstrates superior results compared to traditional BO methods.
Computer Science > Machine Learning arXiv:2602.01445 (cs) [Submitted on 1 Feb 2026 (v1), last revised 16 Feb 2026 (this version, v2)] Title:A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting Authors:Ons Saadallah, Mátyás andó, Tamás Gábor Orosz View a PDF of the paper titled A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting, by Ons Saadallah and M\'aty\'as and\'o and Tam\'as G\'abor Orosz View PDF HTML (experimental) Abstract:Hyperparameter optimization (HPO) plays a central role in the performance of deep learning models, yet remains computationally expensive and difficult to interpret, particularly for time-series forecasting. While Bayesian Optimization (BO) is a standard approach, it typically treats tuning tasks independently and provides limited insight into its decisions. Recent advances in large language models (LLMs) offer new opportunities to incorporate structured prior knowledge and reasoning into optimization pipelines. We introduce LLM-AutoOpt, a hybrid HPO framework that combines BO with LLM-based contextual reasoning. The framework encodes dataset meta-features, model descriptions, historical optimization outcomes, and target objectives as structured meta-knowledge within LLM prompts, using BO to initialize the search and mitigate cold-start effects. This design enables context-aware and stable hyperparameter refinement while exposing the reasoning behind opti...
