A Simple State Space Model Excels at Multivariate Time Series Classification

This research systematically studies structured state space models (SSMs) for time-series classification, comparing complex Mamba-based architectures with simpler diagonal SSMs (S4D). Surprisingly, S4D consistently outperforms Mamba variants in accuracy and efficiency on large-scale benchmarks, challenging the assumption that increased model complexity leads to better performance in this domain.