{"type":"ScholarlyArticle","title":"Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization","authors":[{"name":"Yifan Wang","slug":"yifan-wang","given":"Yifan","family":"Wang","affiliation":""},{"name":"Lifeng Shen","slug":"lifeng-shen","given":"Lifeng","family":"Shen","affiliation":""},{"name":"Shuyin Xia","slug":"shuyin-xia","given":"Shuyin","family":"Xia","affiliation":""},{"name":"Yi Wang","slug":"yi-wang","given":"Yi","family":"Wang","affiliation":""}],"abstract":"Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency.\r\nSimilarity-based methods often suffer from quadratic complexity caused by pairwise distance computations, while deep learning–based approaches typically rely on costly iterative training and a large number of trainable parameters.  In this paper, we propose MSRGC-Net, an efficient time-series\r\nclustering framework that integrates multiscale reservoir computing, granular-ball-based anchoring graph construction, and consensus learning.\r\nMSRGC-Net adopts a training-free reservoir computing paradigm to extract multiscale temporal representations from raw time series without\r\nbackpropagation, significantly reducing computational overhead. To capture the intrinsic structure of the resulting representations, granular-ball\r\ncomputing is employed to adaptively model data distributions via density-consistent regions, yielding compact and robust anchor graph representations. Furthermore, a consensus-based anchoring graph optimization strategy is introduced to effectively align multiscale reservoir representations and integrate complementary information across temporal scales.  Extensive experiments on widely used univariate and multivariate benchmark datasets demonstrate that MSRGC-Net consistently outperforms state-of-the-art methods in clustering performance while maintaining superior computational efficiency.","doi":"10.24963/ijcai.2026/557","url":"https://ijcai.org/proceedings/2026/557","pdf":"https://ijcai.org/proceedings/2026/0557.pdf","markdown":"https://ijcai.org/proceedings/2026/557.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/557","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":557,"pages":{"first":5003,"last":5011},"keywords":["Machine Learning: Clustering"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}