StableAD-TS: A Hybrid Computational Intelligence Framework for Mining Latent Semantic Correlations of Normal Patterns in Multivariate Time Series

Abstract Reliable condition monitoring in cyber-physical systems requires computational intelligence models that recover normal operating semantics from multivariate measurements that are noisy, non-stationary, and semantically sparse. This paper studies the mining of latent semantic correlations among variables from predominantly normal data. We propose StableAD-TS, a symmetric encoder–decoder that learns a noise-suppressed manifold of normal operation and reconstructs from that manifold, so that deviations appear as large residuals. In the encoder, spatiotemporal hybrid attention and Discriminative Semantic Gates (DS-Gates) extract hierarchical cross-variable features under semantic sparsity. In the decoder, stable-component skip connections and offset subtraction retain informative trends while isolating anomalous perturbations; a single-step diffusion perturbation further improves robustness under drift. Reconstruction residuals are used to assess event-level separability of the learned semantics. On four industrial monitoring benchmarks—PSM, SWaT, SMD, and MSL—StableAD-TS attains competitive or superior affiliation precision, recall, and F1 against 18 representative baselines, confirming the benefit of mining latent multivariate information.