Describe-to-Anchor: Profile-Guided Relational Anchoring for Semantically Grounded Multivariate Time Series Clustering

Deep clustering of unlabeled multivariate time series (MTS) increasingly relies on self-supervised representations derived from reconstruction, augmented views, or evolving pseudo-labels. Without an independent semantic reference, these objectives may capture incidental regularities, drift with the model, or reinforce degenerate geometry. Deterministic temporal descriptors provide stable and interpretable knowledge, but using them as inputs or representations creates a mismatch with fine-grained sequence dynamics. This paper, therefore, proposes PRISM (Profile-guided Representation learning with Intrinsic Structure Matching), a Describe-to-Anchor framework that converts deterministic profiles into fixed relational guidance. Because profile prototypes capture only coarse organization, PRISM complements the prototype anchor with a neighborhood anchor that preserves local distinctions. Since descriptors omit fine-grained dynamics, a temporal patch encoder learns from raw MTS while matching both anchors. This division grounds the learned geometry in explicit temporal characteristics without limiting its representational capacity. Experiments on 30 UEA benchmark datasets demonstrate strong performance against recent baselines, including the best macro-average score and average rank under all four metrics. Statistical tests, anchor-target diagnostics, and semantic traceability analyses on two real-world datasets further validate fixed profile relations.