AAAI2024不久前放榜，时间序列研究组博士生柳真同学的论文在本次12100篇论文投稿中脱颖而出，成功被录用。AAAI会议作为人工智能领域享有盛誉的顶级会议，由于其业内的认可度高，一直以来深受科研工作者们投稿的青睐。根据官方信息，本次会议主赛道的录稿率为23.75%。
论文标题：《Diffusion Language-Shapelets for Semi-supervised Time-Series Classification》
论文摘要：Semi-supervised time-series classification could effectively alleviate the issue of lacking labeled data. However, existing approaches usually ignore model interpretability, making it difficult for humans to understand the principles behind the predictions of a model. Shapelets are a set of discriminative subsequences that show high interpretability in time series classification tasks. Shapelet learning based methods have demonstrated promising classification performance. Unfortunately, without enough labeled data, the shapelets learned by existing methods are often poorly discriminative, and even dissimilar to any subsequence of the original time series. To address this issue, we propose the Diffusion Language-Shapelets model (DiffShape) for semi-supervised time series classification. In DiffShape, a self-supervised diffusion learning mechanism is designed, which uses real subsequences as a condition. This helps to increase the similarity between the learned shapelets and real subsequences by using a large amount of unlabeled data. Furthermore, we introduce a contrastive language-shapelets learning strategy that improves the discriminability of the learned shapelets by incorporating the natural language descriptions of the time series. Experiments have been conducted on the UCR time series archive, and the results reveal that the proposed DiffShape method achieves state-of-the-art performance and exhibits superior interpretability over baselines.
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