LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 23 Mar 2024.
Builds on
9 of 15 references.
References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2020Longformer: The Long-Document Transformercited by 3,413
- 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
- 2018The UCR Time Series Archivecited by 431
- 2021Do Prompt-Based Models Really Understand the Meaning of their Prompts?cited by 326
- 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 169
- 2022Unveiling the higher-order organization of multivariate time seriescited by 17
- 2023BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profilescited by 2
Built on by
Citing papers per year: 2025: 1.
1 paper.