Efficient Model Selection for Time Series Forecasting via LLMs
Authors: name: Wang Wei, name: Tiankai Yang, name: Hongjie Chen, name: Ryan A. Rossi, name: Yue Zhao, name: Franck Dernoncourt, name: Hoda Eldardiry

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.