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Improving the accuracy and robustness of predictive models is essential for reliable long-term forecasting. In this paper, we propose E-FuTuRe, a novel approach that combines fuzzy ensemble models with transfer learning strategies and differential equations to enhance future predictions. Our approach demonstrates significant improvements in predictive performance. Specifically, our experiments show that E-FuTuRe achieves an accuracy of 91.0%, precision of 89.5%, recall of 88.8%, and F1-score of 89.2%, outperforming existing methods. E-FuTuRe is particularly well-suited for domains such as finance, healthcare, and environmental monitoring, where accurate and dependable forecasts are crucial. The unique contributions of this work include the integration of fuzzy logic to manage uncertainty, transfer learning to leverage pre-trained models, and differential equations to model temporal dependencies. This combination results in a robust and precise prediction framework that consistently surpasses traditional predictive models and state-of-the-art methods. By effectively handling uncertain data, utilizing knowledge from related domains, and capturing the dynamic behavior of variables over time, E-FuTuRe provides more reliable future predictions. Additionally, the inclusion of advanced ensemble learning techniques such as weighted averaging and boosting further enhances the model's performance, ensuring a comprehensive approach to predictive analytics. Our results indicate that E-FuTuRe’s innovative approach offers a substantial improvement over existing methodologies, making it a valuable tool for predictive analytics in diverse fields. The potential for adaptation and scalability across various domains highlights the practical implications and future research opportunities presented by this work.

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E-FuTuRe: Enhancing Fuzzy Ensemble Models for Future Predictions Using Transfer Learning Techniques with Differential Equations Approach

  • 会议论文

  • 发表日期：

    2025年

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  • V. Kavitha,
  • M. Josephine Rebecca,
  • A. P. Pushpalatha,
  • M. Clement Joe Anand,
  • K. Arun Prakash,
  • Tripti Tiwari

摘要

Improving the accuracy and robustness of predictive models is essential for reliable long-term forecasting. In this paper, we propose E-FuTuRe, a novel approach that combines fuzzy ensemble models with transfer learning strategies and differential equations to enhance future predictions. Our approach demonstrates significant improvements in predictive performance. Specifically, our experiments show that E-FuTuRe achieves an accuracy of 91.0%, precision of 89.5%, recall of 88.8%, and F1-score of 89.2%, outperforming existing methods. E-FuTuRe is particularly well-suited for domains such as finance, healthcare, and environmental monitoring, where accurate and dependable forecasts are crucial. The unique contributions of this work include the integration of fuzzy logic to manage uncertainty, transfer learning to leverage pre-trained models, and differential equations to model temporal dependencies. This combination results in a robust and precise prediction framework that consistently surpasses traditional predictive models and state-of-the-art methods. By effectively handling uncertain data, utilizing knowledge from related domains, and capturing the dynamic behavior of variables over time, E-FuTuRe provides more reliable future predictions. Additionally, the inclusion of advanced ensemble learning techniques such as weighted averaging and boosting further enhances the model's performance, ensuring a comprehensive approach to predictive analytics. Our results indicate that E-FuTuRe’s innovative approach offers a substantial improvement over existing methodologies, making it a valuable tool for predictive analytics in diverse fields. The potential for adaptation and scalability across various domains highlights the practical implications and future research opportunities presented by this work.