AI RESEARCH
AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
arXiv CS.LG
•
ArXi:2606.03631v1 Announce Type: new Multivariate time series classification (MTSC) is pivotal in high-stakes domains, such as clinical diagnosis and industrial fault detection, where safe deployment necessitates transparent decision-making. However, isolating the temporal segments that drive model predictions is challenging because discriminative signals in real-world time series are typically sparse, heterogeneous, and heavily obscured by background noise. This paper, therefore, proposes AnchorMoE, an interpretable-by-construction classification framework.