Large language models are few-shot multivariate time series classifiers

• Published in: Data mining and knowledge discovery Vol. 39; no. 5; p. 66
• Main Authors: Chen, Yakun, Li, Zihao, Yang, Chao, Wang, Xianzhi, Xu, Guandong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2025; Springer Nature B.V
• Subjects: Architecture; Artificial Intelligence; Chemistry and Earth Sciences; Classification; Computer Science; Data Mining and Knowledge Discovery; Datasets; Deep learning; Design; Electrocardiography; Information Storage and Retrieval; Knowledge; Large language models; Machine learning; Multivariate analysis; Physics; Statistics for Engineering; Time series; Multivariate time series classification; Large language model; Few-shot learning; Fine-tuning; Patch-wise temporal convolution
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-025-01145-z

Large Language Models (LLMs) are widely applied in time series analysis. Yet, their utility in few-shot classification—a scenario with limited training data—remains unexplored. We aim to leverage the pre-trained knowledge in LLMs to overcome the data scarcity problem within multivariate time series. To this end, we propose LLMFew , an LLM-enhanced framework, to investigate the feasibility and capacity of LLMs for few-shot multivariate time series classification (MTSC). We first introduce a P atch-wise T emporal C onvolution Enc oder (PTCEnc) to align time series data with the textual embedding input of LLMs. Then, we fine-tune the pre-trained LLM decoder with Low-rank Adaptations (LoRA) to enable effective representation learning from time series data. Experimental results show our model consistently outperforms state-of-the-art baselines by a large margin, achieving 125.2% and 50.2% improvement in classification accuracy on Handwriting and EthanolConcentration datasets, respectively. Our results also show LLM-based methods achieve comparable performance to traditional models across various datasets in few-shot MTSC, paving the way for applying LLMs in practical scenarios where labeled data are limited. Our code is available at https://github.com/junekchen/llm-fewshot-mtsc .