Industrial Nonstationary Multivariate Time Series Forecasting via Frequency-Domain Decomposition and Seasonal-Trend Bidirectional Interaction

In complex industrial production processes, accurate multivariate time series forecasting is essential for operational control, process optimization, and safety assurance. However, the inherent nonstationarity and multiscale characteristics of industrial time series present a significant challenge to achieving accurate predictions. To this end, this article proposes a novel method for multivariate nonstationary industrial time series forecasting, which integrates frequency-domain decomposition with season-trend bidirectional interaction. First, a learnable frequency-domain decomposition module is designed to decouple the input sequences adaptively into distinct components that possess different frequency properties. Second, a bidirectional gated interaction module is developed, which facilitates dynamic and controlled information exchange between the seasonal and trend encoding branches. Furthermore, a loss function incorporating trend consistency constraints is established to enhance the model's capability in capturing the dynamic morphologies of the sequences. Experimental results on multiple real-world industrial datasets demonstrate that our model outperforms existing state-of-the-art methods on three industrial datasets, thereby validating its effectiveness and practical value in forecasting complex industrial time series.