  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.
