MISMS: a multistep-ahead and interpretable sequential modelling scheme for the long-term dynamic forecasting in process industries

Wenshuo Song, Weihua Cao, Yan Yuan, Kang-Zhi Liu and Min Wu

International Journal of Systems Science, 2025, vol. 56, issue 11, 2570-2587

Abstract: This paper introduces Multistep-ahead and Interpretable Sequential Modeling Scheme (MISMS), a pioneering approach for long-term dynamic forecasting that enhances model interpretability in process industries. MISMS is distinguished by its innovative integration of multivariate analysis, multistep-ahead forecasting, and sequential model interpretability. The scheme incorporates knowledge embedding and Exploratory Data Analysis (EDA) to structure multiple variables and extract their temporal attributes. Based on this groundwork, sequence-to-sequence (Seq2Seq) submodels are developed to capture domain-wide dynamics rather than one single value. Crucially, MISMS has pioneered the application of sequence interpretation for model decomposition and transparency, improving interpretability. The proposed scheme employs different temporal models in practical scenarios and can ensure a better prediction with long-term accuracy and stability. Additionally, discussions on the model explainer's expected and unexpected outcomes are conducted, providing potential avenues for future research in process industries.

Date: 2025
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DOI: 10.1080/00207721.2025.2449590

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