{"type":"ScholarlyArticle","title":"Learning Hyperspherical Time–Frequency Representations for Time-Series Out-of-Distribution Detection","authors":[{"name":"Willian T. Lunardi","slug":"willian-t-lunardi","given":"Willian T.","family":"Lunardi","affiliation":""},{"name":"Samridha Shrestha","slug":"samridha-shrestha","given":"Samridha","family":"Shrestha","affiliation":""},{"name":"Martin Andreoni","slug":"martin-andreoni","given":"Martin","family":"Andreoni","affiliation":""}],"abstract":"Out-of-distribution (OOD) detection for time-series data remains comparatively underexplored compared to vision and language, with a limited principled understanding of how supervised time-series representations can be leveraged for reliable detection under distributional shifts. This work formulates time-series OOD detection as representation learning with hyperspherical embeddings, where class-conditional structure is induced by a von Mises–Fisher (vMF) likelihood–based objective on the unit sphere. The learned representation combines time- and frequency-domain views of the input signal via domain-specific encoders, integrating them into a joint embedding space for OOD detection. Detection uses distance-based scores over the learned embeddings, including k-nearest neighbors (k-NN) and Mahalanobis scores. We evaluate the approach at scale on the complete UCR and UEA time-series archives under a cross-dataset protocol. Empirical results show consistent improvements under both k-NN and Mahalanobis scoring over strong contrastive-learning and post-hoc baselines in the same setting. Code is available at https://github.com/tiiuae/hypertf-time-series-ood.","doi":"10.24963/ijcai.2026/321","url":"https://ijcai.org/proceedings/2026/321","pdf":"https://ijcai.org/proceedings/2026/0321.pdf","markdown":"https://ijcai.org/proceedings/2026/321.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/321","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":"Data Mining","track":"Main Track","number":321,"pages":{"first":2888,"last":2896},"keywords":["Machine Learning: Representation learning","Data Mining: Anomaly/outlier detection","Machine Learning: Open-World/Open-Set/OOD Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}