STEP: Learning STructured Embeddings for Progressive Time Series

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 where progression manifests along manifolds anchored by fixed prototype vectors. This structure yields latent-space indicators that quantify progression in a human-meaningful way without proxy labels. We evaluate the approach against the state of the art on diverse domains, including industrial degradation, robotic tasks, and neural activity, validating three key capabilities: (1) end-state prediction, (2) multi-step forecasting, and (3) interpretable phase separation. Our method matches or improves over black-box counterparts on all of these while providing transparency about the underlying mechanisms. A simple linear regressor on top of the learned indicators is competitive with deep architectures, providing direct quantitative evidence that the underlying state is encoded in a geometrically accessible form. Code is available at https://anonymous.4open.science/r/LRPTS-9300/README.md.