Multi-scale spatiotemporal information fusion for long-term industrial time series forecasting and applications

Time series data collected from real-world frequently exhibit intricate multi-scale dynamics, which hinder accurate long-term forecasting. To address them, we propose MSSTMixer, a novel Multi-Scale Spatiotemporal Dual-Stream Fusion Model. Specifically, the framework first employs a multi-scale patching strategy to decompose the input sequence into sub-sequences of varying scales. It then adopts a dual-stream architecture: (i) a spatial stream that uncovers dependencies among variables, and (ii) a temporal stream that extracts short-term fluctuations and long-term trends. To effectively integrate these representations, we devise a two-stage fusion mechanism: (i) features at same-scale are concatenated to obtain preliminary fused embeddings; (ii) multiplicative and differential interaction pathways are designed to exploit complementary patterns across scales. Finally, a rolling-window strategy is employed for long-term forecasting. Experiments on six public benchmarks demonstrate that MSSTMixer consistently outperforms state-of-the-art baselines and achieves superior performance on molten iron quality prediction in an operational blast furnace, highlighting its industrial potential.