arXiv:2605.31061v1 Announce Type: new Abstract: We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transitions such as degradation or task completion. Our approach uses a self-supervised contrastive objective to learn a low-dimensional latent space whose geometry is itself the interpretation: each observation becomes a point on a manifold anchored between two fixed orthogonal prototype vectors, and a trajectory becomes a path across that manifold. From this structure we read a latent compass, the polar coo
This research is published as AI models continue to advance in handling complex data types, and there's a growing need for interpretability in time series analysis for critical applications.
The proposed method offers a novel way to interpret progressive time series, which is crucial for understanding and predicting complex systems like degradation or task completion in industrial and biological contexts.
This approach introduces a self-supervised learning technique that can infer meaningful, structured latent spaces for progressive time series, potentially enabling more accurate predictions and deeper insights into sequential processes.
- · AI researchers
- · Predictive maintenance industry
- · Healthcare diagnostics
- · Manufacturing sector
- · Traditional black-box time series models
- · Systems relying solely on statistical process control
Improved interpretability and predictability for irreversible state transitions in complex systems.
Accelerated development of AI-driven solutions for asset management, disease progression monitoring, and process optimization.
Potential for new forms of human-AI collaboration where AI provides a 'latent compass' or intuitive map for complex processes.
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Read at arXiv cs.LG