Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911326877515776 |
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| author | Tao, Xiaoyu Pan, Tingyue Cheng, Mingyue Luo, Yucong Liu, Qi Chen, Enhong |
| author_facet | Tao, Xiaoyu Pan, Tingyue Cheng, Mingyue Luo, Yucong Liu, Qi Chen, Enhong |
| contents | Time series classification plays a fundamental role in a wide range of real-world applications. Recently, large language models (LLMs) have demonstrated strong generalization and reasoning capacities, but directly applying them to time series classification remains non-trivial due to the representation gap between numerical sequences and linguistic semantics. In this paper, we propose HiTime, a hierarchical LLM-based framework for multimodal time series classification that bridges structured temporal representations with semantic reasoning in a generative paradigm. Specifically, we design a hierarchical sequence feature encoding module composed of a data-specific encoder and a task-specific encoder to extract complementary temporal features. To mitigate the embedding gap between time series representations and textual semantics, we further introduce a semantic space alignment module that jointly performs coarse-grained global modeling and fine-grained cross-modal correspondence. Building upon the above representations, we employ a parameter-efficient supervised fine-tuning strategy to activate the generative classification capability of the algined LLMs, thereby transforming conventional discriminative time series classification into a generative task. Extensive experiments on multiple benchmarks demonstrate that the proposed framework consistently outperforms state-of-the-art baselines. The code is publicly available at https://github.com/Xiaoyu-Tao/HiTime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18686 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification Tao, Xiaoyu Pan, Tingyue Cheng, Mingyue Luo, Yucong Liu, Qi Chen, Enhong Machine Learning Time series classification plays a fundamental role in a wide range of real-world applications. Recently, large language models (LLMs) have demonstrated strong generalization and reasoning capacities, but directly applying them to time series classification remains non-trivial due to the representation gap between numerical sequences and linguistic semantics. In this paper, we propose HiTime, a hierarchical LLM-based framework for multimodal time series classification that bridges structured temporal representations with semantic reasoning in a generative paradigm. Specifically, we design a hierarchical sequence feature encoding module composed of a data-specific encoder and a task-specific encoder to extract complementary temporal features. To mitigate the embedding gap between time series representations and textual semantics, we further introduce a semantic space alignment module that jointly performs coarse-grained global modeling and fine-grained cross-modal correspondence. Building upon the above representations, we employ a parameter-efficient supervised fine-tuning strategy to activate the generative classification capability of the algined LLMs, thereby transforming conventional discriminative time series classification into a generative task. Extensive experiments on multiple benchmarks demonstrate that the proposed framework consistently outperforms state-of-the-art baselines. The code is publicly available at https://github.com/Xiaoyu-Tao/HiTime. |
| title | Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.18686 |