title: Efficient Model Selection for Time Series Forecasting via LLMs
summary: 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.
paper_authors._id: 67ef41e7efcb0a2fbfbb6a32
paper_authors.name: Wang Wei
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paper_authors.name: Tiankai Yang
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paper_authors.fullname: Tiankai Yang
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paper_authors.user: tiankaiy
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paper_authors.name: Hongjie Chen
paper_authors._id: 67ef41e7efcb0a2fbfbb6a35
paper_authors.name: Ryan A. Rossi
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paper_authors.fullname: Ryan A. Rossi
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paper_authors.name: Yue Zhao
paper_authors._id: 67ef41e7efcb0a2fbfbb6a37
paper_authors.name: Franck Dernoncourt
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Franck Dernoncourt
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paper_authors.user: Franck-Dernoncourt
paper_authors._id: 67ef41e7efcb0a2fbfbb6a38
paper_authors.name: Hoda Eldardiry
paper_title: Efficient Model Selection for Time Series Forecasting via LLMs
paper_summary: 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.
paper_ai_summary: LLMs efficiently perform model selection in time series forecasting by leveraging inherent knowledge and reasoning, outperforming traditional techniques and reducing computational overhead.
paper_ai_keywords: Large Language Models LLMs LLaMA GPT Gemini meta-learning time series forecasting performance matrices computational overhead