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arXiv · 2610.23353

RTSM: Adapting Recursive Models to Multivariate Time-Series Classification

Georg Tenzing·Anna Tegon·Luca Benini·Thorir Mar Ingolfsson
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Abstract

Accurate multivariate time-series classification requires extracting temporal features and integrating information across a sequence. Weight-tied refinement offers a way to repeatedly process these features without allocating new parameters at each stage. Applying this principle to time series requires choosing both a temporal representation and a latent-state organization suited to classification. We introduce the Recursive Time-Series Model (RTSM), which combines a learned convolutional temporal tokenizer, single-state weight-tied refinement, and a pooled classification readout. Across all 30 UEA datasets, RTSM achieves 72.81% mean accuracy under validation-based configuration and checkpoint selection, the highest observed mean among the 17 evaluated deep-learning models and 1.60 percentage points above MambaSL. Ablations on eight datasets favor the proposed tokenizer over a simple convolutional embedding and the single-state model over the tested two-state variants. The complete classifier reaches 86.10% mean accuracy versus 84.79% for a separately trained tokenizer-only classifier. These findings identify temporal encoding and state organization as key design choices for adapting recursive architectures to time series. The resulting classifier combines competitive accuracy with a shared refinement module whose parameter count is independent of computational depth.

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Georg Tenzing, Anna Tegon, Luca Benini, Thorir Mar Ingolfsson. 2026-10-16. RTSM: Adapting Recursive Models to Multivariate Time-Series Classification. https://arxiv.org/abs/2610.23353

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