Learning Hyperspherical Time-Frequency Representations for Time-Series Out-of-Distribution Detection
arXiv:2605.31155v1 Announce Type: new 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
The continuous growth of time-series data in various applications necessitates improved anomaly detection methods, driving research into more robust OOD detection techniques.
Improved out-of-distribution detection in time-series data is critical for reliable AI systems in finance, health, and industrial control, where anomalous events can have significant consequences.
This research introduces a novel representation learning approach using hyperspherical embeddings, which could lead to more accurate and reliable OOD detection in inherently sequential data.
- · AI safety researchers
- · Time-series data platform providers
- · Industries relying on anomaly detection
- · Systems with high false positive rates
- · Legacy anomaly detection methods
Enhanced ability to identify critical anomalies and rare events in complex time-series datasets.
Increased trustworthiness and deployment of AI systems in high-stakes time-series applications such as medical diagnostics or predictive maintenance.
Potential for new security applications by flagging unusual patterns in network traffic or system logs with greater precision.
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Read at arXiv cs.LG