Existing backdoor attacks on multivariate time series (MTS) forecasting enforce strict temporal and dimensional coupling between triggers and target patterns, requiring synchronous activation at fixed positions across variables. However, realistic scenarios often demand delayed and variable-specific activation. We identify this critical unmet need and propose TDBA, a temporally decoupled backdoor attack framework for MTS forecasting. By injecting triggers that encode the expected location of the target pattern, TDBA enables the activation of the target pattern at any positions within the forecasted data, with the activation position flexibly varying across different variable dimensions. TDBA introduces two core modules: (1) a position-guided trigger generation mechanism that leverages smoothed Gaussian priors to generate triggers that are position-related to the predefined target pattern; and (2) a position-aware optimization module that assigns soft weights based on trigger completeness, pattern coverage, and temporal offset, facilitating targeted and stealthy attack optimization. Extensive experiments on real-world datasets show that TDBA consistently outperforms existing baselines in effectiveness while maintaining good stealthiness. Ablation studies confirm the controllability and robustness of its design.

翻译：现有的多元时间序列预测后门攻击强制要求触发器与目标模式之间具有严格的时间与维度耦合，需要在不同变量间实现固定位置上的同步激活。然而，实际场景往往需要延迟且针对特定变量的激活。我们识别了这一关键未满足需求，并提出了TDBA——一种用于多元时间序列预测的时间解耦后门攻击框架。通过注入编码了目标模式预期位置的触发器，TDBA能够在预测数据中的任意位置激活目标模式，且激活位置可灵活地随不同变量维度而变化。TDBA引入了两个核心模块：(1) 一种位置引导的触发器生成机制，利用平滑高斯先验生成与预定义目标模式位置相关的触发器；(2) 一个位置感知优化模块，基于触发器完整性、模式覆盖度和时间偏移分配软权重，以促进有针对性且隐蔽的攻击优化。在真实数据集上的大量实验表明，TDBA在保持良好隐蔽性的同时，其攻击效果始终优于现有基线方法。消融研究证实了其设计的可控性与鲁棒性。