LETS-C: Leveraging Text Embedding for Time Series Classification

ACL ARR 2025 February Submission2960 Authors

Abstract: Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time series classification tasks has achieved state-of-the-art (SOTA) performance on standard benchmarks. However, these LLM-based models have a significant drawback due to the large model size, with the number of trainable parameters in the millions. In this paper, we propose an alternative approach to leveraging the success of language modeling in the time series domain. Instead of fine-tuning LLMs, we utilize a text embedding model to embed time series and then pair the embeddings with a simple classification head composed of convolutional neural networks (CNN) and multilayer perceptron (MLP). We conducted extensive experiments on a well-established time series classification benchmark. We demonstrated LETS-C not only outperforms the current SOTA in classification accuracy but also offers a lightweight solution, using only 14.5% of the trainable parameters on average compared to the SOTA model. Our findings suggest that leveraging text embedding models to encode time series data, combined with a simple yet effective classification head, offers a promising direction for achieving high-performance time series classification while maintaining a lightweight model architecture. The implementation code is available at https://anonymous.4open.science/r/LETS-C.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: language embedding, time series, classification, lightweight

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency, Data analysis

Languages Studied: English

Submission Number: 2960