Spatiotemporal Feature Enhancement Adversarial Attack for Multivariate Time Series Prediction

The rise of multivariate time series (MTS) data has made prediction crucial, with deep learning models dominant yet vulnerable to adversarial attacks. These attacks use small perturbations on inputs to cause mispredictions. MTS data's inherent complexity and sensitivity demand stringent perturbation handling. To address this, we propose TFCA, an adversarial attack method focusing on feature dimension impact. TFCA calculates each feature's gradient contribution to predictions independently, establishing a feature importance ranking. It enhances temporal characteristics by integrating multi-dimensional elements (e.g., volatility, cosine direction) to constrain adversarial sample realism. Guided by this ranking, TFCA targets specific attack ratios on subsets of the time series, ensuring attack effectiveness while reducing perturbation size and improving stealthiness. Experiments on real MTS datasets against models (TCN, LSTNet, CNN) demonstrate TFCA's effectiveness, stealthiness, applicability, and transferability. The integration of post hoc explainable AI algorithms also provides interpretability. This research offers insights for enhancing MTS model robustness and security in practice.