Efficient Model Selection for Time Series Forecasting via LLMs
This work addresses the efficiency challenge in model selection for time series forecasting practitioners, though it is incremental as it applies existing LLMs to a known bottleneck.
The paper tackles the problem of costly model selection in time series forecasting by using Large Language Models (LLMs) as a lightweight alternative, demonstrating that this approach outperforms traditional meta-learning techniques and reduces computational overhead.
Model selection is a critical step in time series forecasting, traditionally requiring extensive performance evaluations across various datasets. Meta-learning approaches aim to automate this process, but they typically depend on pre-constructed performance matrices, which are costly to build. In this work, we propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection. Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs. Through extensive experiments with LLaMA, GPT and Gemini, we demonstrate that our approach outperforms traditional meta-learning techniques and heuristic baselines, while significantly reducing computational overhead. These findings underscore the potential of LLMs in efficient model selection for time series forecasting.