A Simple State Space Model Excels at Multivariate Time Series Classification

2026-05-07

arXiv.org

AI Summary

This work investigates whether structured state space models require complex input-dependent mechanisms for multivariate time series classification. Through systematic evaluation of diagonal state space models (S4D) against Mamba-style input-dependent architectures, the study demonstrates that simplified designs can achieve competitive performance. To this end, the authors propose MS4 and its normalized variant MS4N—lightweight models incorporating only linear input projection and channel mixing. Extensive experiments across 59 datasets from MONSTER and UEA benchmarks show that MS4N outperforms Mamba in both accuracy and efficiency while using fewer parameters, and matches or exceeds the performance of state-of-the-art deep learning models that are 2–10 times larger. These results underscore the efficacy and superiority of minimalist architectural design in this domain.

Abstract

Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through input-dependent state transitions, albeit at considerable complexity. However, their application to time-series classification (TSC) has been largely limited to Mamba-style architectures, leaving the broader SSM design space underexplored. We present the first systematic study spanning diagonal SSMs (S4D) and input-dependent SSMs (Mamba family) on large-scale TSC benchmarks, asking whether such complexity is necessary for top performance. Our results reveal a surprising finding: S4D consistently outperforms Mamba-based variants in both accuracy and efficiency, challenging the assumption that increased complexity translates to meaningful gains in TSC. Building on this, we introduce MS4, lightweight modifications to S4D via a linear input projection and channel-mixing mechanism, and MS4N, a normalized variant that stabilizes state dynamics with negligible overhead. Evaluated on 59 datasets across MONSTER (up to 60 million samples, 50K timesteps, 82 classes) and the UEA benchmark, against 15 baselines, MS4 and MS4N consistently outperform Mamba-based models while remaining more efficient, and MS4N matches or surpasses competing deep learning models that are roughly 2x and 10x larger in parameters. These results position lightweight structured SSMs as a compelling alternative to scaling complexity for TSC.

Problem

• multivariate time series classification
• state space models
• model complexity
• S4D
• Mamba

Innovation

• State Space Models
• Time Series Classification
• S4D
• Mamba
• Model Efficiency