A Consistency-Aware Hybrid Static–Dynamic Multivariate Network for Forecasting Industrial Key Performance Indicators

The accurate forecasting of key performance indicators (KPIs) is essential for enhancing the reliability and operational efficiency of engineering systems under increasingly complex security challenges. However, existing approaches often neglect the heterogeneous nature of multivariate time series data, particularly the consistency of measurements and the influence of external factors, which limits their effectiveness in real-world scenarios. In this work, a Consistency-aware Hybrid Static-Dynamic Multivariate forecasting Network (CHSDM-Net) is proposed, which first applies a consistency-aware, optimization-driven segmentation to ensure high internal consistency within each segment across multiple variables. Secondly, a hybrid forecasting model integrating a Static Representation Module and a Dynamic Temporal Disentanglement and Attention Module for static and dynamic data fusion is proposed. For the dynamic data, the trend and periodic components are disentangled and fed into Trend-wise Attention and Periodic-aware Attention blocks, respectively. Extensive experiments on both synthetic and real-world radar detection datasets demonstrated that CHSDM-Net achieved significant improvements compared with existing methods. Comprehensive ablation and sensitivity analyses further validated the effectiveness and robustness of each component. The proposed method offers a practical and generalizable solution for intelligent KPI forecasting and decision support in industrial engineering applications.