Academic Journal
ST-Align: A time series and text alignment framework for cross-subject multivariate time series classification.
| Τίτλος: | ST-Align: A time series and text alignment framework for cross-subject multivariate time series classification. |
|---|---|
| Συγγραφείς: | Wu Z; School of Aeronautic Science and Engineering, Beihang University, Beijing, China., Zhang T; School of Aeronautic Science and Engineering, Beihang University, Beijing, China., Li K; School of Aeronautic Science and Engineering, Beihang University, Beijing, China., Li Y; School of Aeronautic Science and Engineering, Beihang University, Beijing, China. Electronic address: liyuangan@buaa.edu.cn. |
| Πηγή: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Sep; Vol. 201, pp. 108913. Date of Electronic Publication: 2026 Mar 28. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| Ιατρικοί όροι (MeSH): | Classification Algorithms*, Humans ; Algorithms ; Large Language Models ; Multivariate Analysis ; Semantics ; Time Factors |
| Περίληψη: | Multivariate Time Series Classification (MTSC) aims to discern temporal dynamics among variables to classify time series. However, existing MTSC methods predominantly focus on unimodal data, thereby neglecting the rich semantic information embedded within the associated text labels. This limitation hampers the ability to comprehend complex patterns and generalize effectively, ultimately resulting in suboptimal performance in real-world cross-subject scenarios. To address these issues, we propose a time series and text alignment framework (ST-Align) for cross-subject multivariate time series classification. ST-Align leverages the semantic alignment between time series and text labels to improve classification accuracy in semantic spaces, while harnessing the prior knowledge of a large language model (LLM). Specifically, we introduce a two-stage alignment approach. In the first stage, a fine-grained token alignment is employed to capture local information. Inspired by contrastive learning, the captured information is then contrasted with the original text labels to achieve a hybrid Token-Prototype alignment. Extensive experiments on four cross-subject datasets demonstrate that ST-Align achieves state-of-the-art (SOTA) performance on unseen datasets. Moreover, comparative analyses between models with and without the LLM demonstrate their effectiveness, achieving average accuracies of 86.3% and 88.4%, respectively. (Copyright © 2026. Published by Elsevier Ltd.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Cross-modal alignment; Large language model (LLM); Multivariate time series classification |
| Entry Date(s): | Date Created: 20260403 Date Completed: 20260612 Latest Revision: 20260623 |
| Update Code: | 20260623 |
| DOI: | 10.1016/j.neunet.2026.108913 |
| PMID: | 41930550 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!