Multifrequency Decoupling Network With Dual-Stage Shift Segmental Modeling for Long-Term Prediction of Industrial Parameters

Long-term prediction of key parameters is essential for the control and optimization of industrial processes with time-delay characteristics. Such prediction remains challenging as industrial time series typically exhibit complex nonlinearity, sparse information, imprecision, and nonstationarity due to complex processes dynamics and environmental instability. Existing modeling paradigms require enhancement to effectively decouple confounding factors and adapt to unknown uncertainties. To this end, we propose a multifrequency decoupling network with dual-stage shift segmental modeling (DS3-MFDNet). The network begins by decomposing the industrial data into distinct frequency components to isolate and manage its complex nonlinearity. Then, we propose a dual-stage shift segmental modeling method for mid-frequency component. This method effectively reduces information sparsity and mitigates the impact of random noise by leveraging contextual information. Furthermore, a position-weighted directional alignment loss is designed to facilitate the training process during the second shift stage. Finally, we design a reversible dual-band condition adaptive normalization mechanism to address the data nonstationarity. Across different prediction lengths, DS3-MFDNet achieves an average improvement of 3–4% over the best baseline on the industrial power distribution process dataset and 6%–8% on the practical loosening and conditioning process dataset, demonstrating the effectiveness of the proposed method.