With the rapid advancement of artificial intelligence technologies in the era of big data, time series forecasting has become indispensable in critical fields such as environmental monitoring and financial market analysis. However, the existing forecasting models often encounter performance limitations when extracting high-dimensional features and generally cannot dynamically focus on critical information within long-term sequences. To address these challenges, this study proposes a multi-head attention fusion model (MAFM) designed to enhance the predictive accuracy and modelling capability for high-dimensional and nonlinear data across diverse application scenarios. Experiments were conducted on two heterogeneous datasets from the environmental and financial domains. After the key hyperparameters of the MAFM were optimized through an orthogonal experimental design, the model achieved coefficients of determination exceeding 0.90 on both datasets. Furthermore, the results of four comparative experiments demonstrate that the MAFM consistently outperforms traditional machine learning models, including support vector regression and extreme gradient boosting, as well as state-of-the-art deep learning models such as long short-term memory, temporal convolutional networks, and transformers. Compared with the best-performing baseline model on each sub-dataset, the MAFM reduced the mean squared error by 44.4%, 8.3%, 29.4%, and 65.5%, respectively, highlighting its superior predictive performance and strong generalization capability. In summary, the proposed MAFM provides an efficient, robust, and interpretable solution for time series forecasting tasks across multiple domains. Its outstanding performance demonstrates significant potential for practical applications in environmental monitoring, financial forecasting, and other real-world scenarios.
Zhenyu Song, Yunuo Zhang, Zenan Lu et al.· Mathematics· 0 citations
Experimental determination of flash points (FPs) for liquid mixtures is laborious and costly, highlighting the need for reliable predictive approaches for safety assessment and engineering applications. Although numerous models have been reported for binary miscible mixtures, most rely on fixed model parameters or empirical correlations, which limits their ability to capture the nonlinear relationship between molecular structure and FP. In this study, a quantitative structure-property relationship (QSPR) framework that tightly integrates differential evolution (DE) with support vector regression (SVR) was developed to predict the FP values of binary miscible mixtures, where DE was employed to globally optimize key SVR hyperparameters and enhance model generalization capability. A dataset consisting of 332 compositions from 33 binary mixtures formed by pairwise combinations of 20 pure components was employed, and multiple molecular descriptor representation strategies were deliberately adopted to construct distinct DE-SVR models, enabling a systematic investigation of the combined effects of descriptor representation and model optimization on predictive performance. Three DE-SVR models were established based on different descriptor sets, and their predictive accuracy, robustness, and stability were comprehensively evaluated. Among them, the model constructed using physicochemical parameters exhibited the best overall performance. Comparative analyses with existing FP prediction methods reported in the literature further confirmed the effectiveness and superiority of the proposed DE-SVR-based models. The results of this study provide a practical tool for FP estimation of binary mixtures and offer valuable insights into the joint roles of model optimization and molecular representation in mixture property prediction.
Shuangyu Song, Xiaoya Song, Xingqian Chen et al.· Journal of Molecular Graphic...· 0 citations
