AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
2026-06-02 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors address the challenge of understanding which parts of a multivariate time series influence a model's decision, especially when important signals are rare and hidden in noise. They propose AnchorMoE, a model that breaks the time series into segments and assigns them to different 'experts' specialized in certain patterns, making the decision process clear and interpretable. To improve this clarity, they use a method ensuring these experts focus on different features and reduce noise influence through a reliability gate. Their experiments show that AnchorMoE performs well on both real and simulated data while providing transparent, segment-based explanations.
Multivariate Time Series ClassificationMixture-of-Experts (MoE)InterpretabilityTemporal SegmentsSparse SignalGeometric Orthogonality ConstraintUncertainty-aware Reliability GateAdditive DecompositionBackground NoiseAnte-hoc Transparency
Authors
Tao Xie, Zexi Tan, Haoyi Xiao, Mengke Li, Yiqun Zhang, Yang Lu, Cuie Yang, Yiu-ming Cheung
Abstract
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. Built upon a Mixture-of-Experts (MoE) architecture, AnchorMoE encodes multi-view representations of local patches and routes them to specialized experts, ensuring that the final prediction is formulated as an exact additive decomposition over the input segments, facilitating ante-hoc transparency rather than relying on post-hoc estimations. To maintain the reliability of this decomposition under sparse signal distributions, we introduce a geometric orthogonality constraint that penalizes representational redundancy, compelling distinct experts to specialize in heterogeneous predictive patterns. Furthermore, an uncertainty-aware reliability gate is designed to dynamically calibrate the contribution of each segment, effectively suppressing residual background noise. Extensive experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance while faithfully grounding its decisions in the raw time series.