Simple State Space Models Outperform Complex Variants in Time Series Classification

2026-05-28

A new study reveals that simpler structured state space models (SSMs), specifically S4D, consistently outperform more complex Mamba-based architectures on large-scale time series classification benchmarks. Researchers introduced lightweight modifications, MS4 and MS4N, which further enhance performance and efficiency.

Source: arXiv · cs.LG

Reported by VERA Newswire.