Learning Hyperspherical Time-Frequency Representations for Time-Series Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2605.31155v1
• Date: Fri, 29 May 2026 11:04:34 GMT
• Title: Learning Hyperspherical Time-Frequency Representations for Time-Series Out-of-Distribution Detection
• Abstract summary: This work formulates time-series OOD detection as representation learning with hyperspherical embeddings.<n>The learned representation combines time- and frequency-domain views of the input signal via domain-specific encoders.<n>We evaluate the approach at scale on the complete UCR and UEA time-series archives under a cross-dataset protocol.
• Score: 0.2160500132391863
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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