Multirate Industrial Process Forecasting With Hybrid Deep Learning and Adaptive Filtering

作者：Xianyao Han, Wen Yu, Yao Jia, Tianyou Chai · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2025 · DOI：10.1109/tnnls.2025.3631923 · 被引用次数：2 · 研究领域：Forecasting Techniques and Applications、Time Series Analysis and Forecasting、Stock Market Forecasting Methods

Multirate industrial processes pose significant challenges for accurate forecasting due to varying sampling frequencies and missing data. This article proposes a novel hybrid deep learning framework that effectively addresses these issues. Our approach uses a combination of time series decomposition, inverted transformer (iTransformer)-based feature extraction, and a modified minimal gated unit (MGU) network. To handle missing quality variables, we introduce a robust adaptive parameter update algorithm based on dead-zone Kalman filtering. Through extensive experiments conducted on real-world industrial datasets, our method achieves a mean absolute error (MAE) reduction of 61.42%, a root-mean-square error (RMSE) reduction of 64.11%, and a high qualification rate improvement of 14.73% compared to the average performance of state-of-the-art technologies, thereby outperforming existing state-of-the-art techniques in terms of both forecasting accuracy and robustness.