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<p>Prediction problems regularly exist in practical application problems, where real application systems are usually complex. For multi-model data in complex systems, effectively identifying the patterns of each data set is significant for subsequent prediction. This paper focuses on the prediction problem of multi-model data and proposes an adaptive regression algorithm (CFM-MSVR) that combines a clustering feedback mechanism with improved support vector regression. The clustering feedback mechanism (CFM) clusters samples based on their residuals in each forecasting model, enabling the discovery of original data generation models. Meanwhile, it can intelligently estimate the number of clusters based on the number of samples in each cluster, reducing computational cost and dependence on empirical settings. In the regression stage, the multi-model support vector regression (MSVR) leverages the non-dominated sorting genetic algorithm II (NSGA-II) to optimise the parameters of the support vector regression, thereby improving the generalisation of each sub-model. The proposed method is evaluated on a simulated dataset, four real-world datasets, and the 2012 Global Energy Forecasting Competition dataset. Results show that CFM-MSVR achieves a MAPE% of 1.52 on the energy prediction task, demonstrating its strong performance in complex forecasting scenarios.</p>

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An adaptive regression algorithm with a clustering process for multi-modal data prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月26日

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  • Shangrui Zhao,
  • Weiqi Yu,
  • Yulu Wu,
  • Jinran Wu,
  • Xi’an Li,
  • You-Gan Wang

摘要

Prediction problems regularly exist in practical application problems, where real application systems are usually complex. For multi-model data in complex systems, effectively identifying the patterns of each data set is significant for subsequent prediction. This paper focuses on the prediction problem of multi-model data and proposes an adaptive regression algorithm (CFM-MSVR) that combines a clustering feedback mechanism with improved support vector regression. The clustering feedback mechanism (CFM) clusters samples based on their residuals in each forecasting model, enabling the discovery of original data generation models. Meanwhile, it can intelligently estimate the number of clusters based on the number of samples in each cluster, reducing computational cost and dependence on empirical settings. In the regression stage, the multi-model support vector regression (MSVR) leverages the non-dominated sorting genetic algorithm II (NSGA-II) to optimise the parameters of the support vector regression, thereby improving the generalisation of each sub-model. The proposed method is evaluated on a simulated dataset, four real-world datasets, and the 2012 Global Energy Forecasting Competition dataset. Results show that CFM-MSVR achieves a MAPE% of 1.52 on the energy prediction task, demonstrating its strong performance in complex forecasting scenarios.