Abstract:Multivariate time series (MTS) classification is foundational to pervasive computing and financial analysis, yet existing multi-scale paradigms are often constrained by suboptimal representation fidelity. We identify two critical bottlenecks: temporal non-causality in standard encoders that induces temporal confounding in non-stationary dynamics, and the absence of explicit channel saliency mechanisms that allows noise to contaminate the latent space. To address these challenges, we propose the Causal Attention and Spatio-temporal Encoder Network (CASE-NET), an architecture designed for structural manifold pre-conditioning. CASE-NET synergizes a Causal Temporal Encoder, which enforces physical arrow-of-time constraints via masked self-attention and causal convolutions, with an Adaptive Channel Recalibration module functioning as an information bottleneck to suppress detrimental noise. Comprehensive evaluations across six heterogeneous domains demonstrate that CASE-NET establishes new state-of-the-art benchmarks on four tasks, achieving a peak accuracy of 98.6% on the AWR dataset and superior robustness in non-stationary regimes.
| Comments: | 9 pages, 6 figures, 2 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.22043 [cs.LG] |
| (or arXiv:2605.22043v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22043 arXiv-issued DOI via DataCite (pending registration) |
From: Yating Cui [view email]
[v1]
Thu, 21 May 2026 06:30:35 UTC (956 KB)
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