LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
This work addresses the challenge of applying language models to time series data for classification, but it appears incremental as it focuses on evaluating existing models with prompts rather than introducing a new method.
The study tackled the problem of adapting pre-trained language models for zero-shot time series classification by developing the LAMPER framework, and found that its feature representation capacity is limited by the maximum input token threshold of the models, as tested on 128 datasets from the UCR archive.
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.