{"type":"ScholarlyArticle","title":"CASE-Net: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification","authors":[{"name":"Fan Zhang","slug":"fan-zhang","given":"Fan","family":"Zhang","affiliation":""},{"name":"Yating Cui","slug":"yating-cui","given":"Yating","family":"Cui","affiliation":""},{"name":"Hua Wang","slug":"hua-wang","given":"Hua","family":"Wang","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/354","url":"https://ijcai.org/proceedings/2026/354","pdf":"https://ijcai.org/proceedings/2026/0354.pdf","markdown":"https://ijcai.org/proceedings/2026/354.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/354","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":354,"pages":{"first":3187,"last":3195},"keywords":["Machine Learning: Deep learning architectures","Machine Learning: Time series and data streams","Data Mining: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}