LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
- URL: http://arxiv.org/abs/2403.15875v1
- Date: Sat, 23 Mar 2024 15:52:37 GMT
- Title: LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
- Abstract summary: This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts.
- Score: 0.4821250031784094
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification. We deploy LAMPER in experimental assessments using 128 univariate TS datasets sourced from the UCR archive. Our findings indicate that the feature representation capacity of LAMPER is influenced by the maximum input token threshold imposed by PLMs.
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