Efficient Model Selection for Time Series Forecasting via LLMs
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.
67ef41e7efcb0a2fbfbb6a32
Wang Wei
extracted_pending
2025-04-04T02:20:24.253Z
670826649e319cca029ff240
/avatars/6d12b3abf75f714d75d1775d88885345.svg
rtfvbhkuj
user
wwdd7718
67ef41e7efcb0a2fbfbb6a33
Tiankai Yang
extracted_confirmed
2025-04-04T02:25:15.747Z
66e4e50a52356419c4a1ad14
/avatars/4be3ce17671785cbe7126b9c1141478b.svg
Tiankai Yang
user
tiankaiy
67ef41e7efcb0a2fbfbb6a34
Hongjie Chen
67ef41e7efcb0a2fbfbb6a35
Ryan A. Rossi
admin_assigned
2025-04-04T07:23:05.421Z
62a3ab83e4dd6252344d27cd
/avatars/7ca8510f70a58dc207b104240e30c35c.svg
Ryan A. Rossi
user
ryanrossi
67ef41e7efcb0a2fbfbb6a36
Yue Zhao
67ef41e7efcb0a2fbfbb6a37
Franck Dernoncourt
extracted_confirmed
2025-04-04T02:34:51.212Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
67ef41e7efcb0a2fbfbb6a38
Hoda Eldardiry
Efficient Model Selection for Time Series Forecasting via LLMs
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.
LLMs efficiently perform model selection in time series forecasting by leveraging inherent knowledge and reasoning, outperforming traditional techniques and reducing computational overhead.
Large Language Models LLMs LLaMA GPT Gemini meta-learning time series forecasting performance matrices computational overhead