Uncertainty-aware multi-modal time series anomaly detection via reduced-order evidence fusion

Time series anomaly detection (TSAD) is crucial for ensuring the reliability of safety-critical systems. While recent multi-view approaches combining time and frequency domains have advanced performance, they still face important representation and fusion bottlenecks. Specifically, conventional linear spectral mappings may introduce representation distortion when decoding complex non-linear frequency shifts. Furthermore, by restricting the feature space to purely numerical modalities, existing deterministic models lack the global operational semantics required to contextualize operational shifts. This semantic void can lead to inter-modal cognitive conflicts and overconfident misclassifications under noisy environments, whereas traditional probabilistic uncertainty estimation methods remain computationally expensive for real-time TSAD. To address these limitations, this paper proposes a Tri-modal Evidential Synergy Network (TES-Net). First, TES-Net leverages a multi-order Kolmogorov-Arnold network alongside large language model (LLM)-extracted global semantics to adaptively decode non-linear spectral fluctuations and bridge the heterogeneous semantic gap. Second, to resolve inter-modal conflicts efficiently, we design a Tri-modal Evidential Fusion module grounded in Dempster-Shafer evidence theory. This mechanism explicitly quantifies modality-level epistemic uncertainty via mass functions in a single deterministic forward pass, dynamically discounting corrupted modalities through expectation aggregation. Finally, a semantic-gated correlation mechanism employs the global textual prior to modulate local inter-variate physical topologies, differentiating genuine anomalies from benign operational transitions. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves competitive performance compared with recent baselines.