AI RESEARCH
A Simple State Space Model Excels at Multivariate Time Series Classification
arXiv CS.LG
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ArXi:2605.27406v1 Announce Type: new Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures nstrating 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.