Work

LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2403.15875

Authors: Du, Zhicheng, Xie, Zhaotian, Tong, Yan, Qin, Peiwu

Institutions: Tsinghua Shenzhen International Graduate School, Tsinghua University, Wuhan University of Technology

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G1454969451, G3148181842, G6931920518

OpenAlex ID: W4393241998

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