Long-Term Series Forecasting for Industrial Processes Based on Multiscale Hybrid Decomposition Feature Extraction Network

Yue Wang, Yongming Han, Xuan Hu, Zhiqiang Geng

Abstract: Long-term series forecasting (LTSF) plays a crucial role in energy efficiency analysis and optimization in industrial production processes. However, due to the complexity and nonstationarity of industrial production data, the fluctuations and transformations between data are deeply mixed, and the variables interact with each other, making prediction extremely challenging. Therefore, this article proposes a novel multiscale hybrid decomposition feature extraction network (MHDN) for industrial long-term series prediction. The MHDN consists of the multiscale feature extraction (MFE) and the time-mixing predictor (TMP) to fully extract multiscale temporal features. The MHDN breaks the traditional operation of using decomposition as a preprocessing block and uses time series decomposition as a basic internal block of deep models. Specifically, the MFE extracts features from seasonal and trend components separately, achieving full extraction of time patterns and decoupling of data fluctuations from complex process data. The TMP integrates multiple predictors in the time domain and the feature domain and uses residual connections to avoid information loss, effectively utilizing multiscale information for complementary prediction. Finally, the MHDN is validated on six benchmarks and an actual industrial production dataset. The experimental results show that compared with the current baseline, the MHDN method achieves state-of-the-art results, reducing the average mean square error and average absolute error by at least 7.8% and 5.6%, which can effectively guide industrial production.