FUSE: Adaptive Frequency-Temporal Unified Semantic Attacks for Time Series Forecasting

Time series forecasting (TSF) models are widely deployed in critical applications, yet their adversarial robustness remains insufficiently explored. Existing attacks mainly rely on time-domain perturbations or untargeted attacks. They overlook the structured vulnerability of forecasting models across spectral and temporal dimensions, and focus only on numerical prediction errors, without analyzing model sensitivity across different representation spaces. In this paper, we propose a Frequency-temporal Unified SEmantic attack framework for time series forecasting (FUSE). We first reveal that TSF models do not exhibit uniform sensitivity across the frequency spectrum, but instead exhibit varying vulnerability across multiple frequency bands. Based on this observation, we design an adaptive full-spectrum perturbation strategy to identify and exploit critical frequency components. However, frequency-domain perturbations alone are insufficient, as they induce global and stationary disturbances, while TSF models rely on localized and non-uniform temporal dependencies. To address this mismatch, we introduce a frequency-temporal alignment mechanism that couples spectral sensitivity with temporal importance. Finally, we extend adversarial targets from numerical deviation to semantic-level manipulation of trends and periodic patterns. Extensive experiments on four datasets demonstrate that FUSE consistently outperforms existing methods in attack effectiveness, transferability, and semantic controllability across diverse forecasting models and datasets. Our code is available at https://anonymous.4open.science/r/FUSE-3353/.