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Forecasting stock market indices is challenging due to the dynamic and non-stationary nature of financial data. Traditional machine learning methods often struggle to capture these complexities, limiting their accuracy. In this paper, we propose two novel dynamic meta-learning ensembles—DME-PC and DME-HC, to address the critical challenge of ensemble member selection and adapting the prediction to each predicted data point, to enhance accuracy. To select ensemble members, DME-PC employs pairwise comparisons with statistical testing while DME-HC uses hierarchical clustering. To adapt the ensemble to the characteristics of the predicted data points, DME-PC and DME-HC combine dynamically the ensemble member predictions based on their performance on recent data and predicted future performance. Experiments on four financial datasets for 10 years and comparison with statistical, machine learning and deep learning methods showed the superior performance of the proposed dynamic ensembles. The best results were obtained with DME-HC (EWA), which utilizes hierarchical clustering for ensemble member selection and exponentially weighted average on most recent errors. Our results highlight the potential of the proposed dynamic ensembles to effectively capture temporal patterns in financial data and support better investment decisions.

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Dynamic Meta-learning Ensemble with Pairwise Comparisons and Hierarchical Clustering for Financial Time Series Forecasting

  • 会议论文

  • 发表日期：

    2026年

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  • Hanxue Yao,
  • Irena Koprinska

摘要

Forecasting stock market indices is challenging due to the dynamic and non-stationary nature of financial data. Traditional machine learning methods often struggle to capture these complexities, limiting their accuracy. In this paper, we propose two novel dynamic meta-learning ensembles—DME-PC and DME-HC, to address the critical challenge of ensemble member selection and adapting the prediction to each predicted data point, to enhance accuracy. To select ensemble members, DME-PC employs pairwise comparisons with statistical testing while DME-HC uses hierarchical clustering. To adapt the ensemble to the characteristics of the predicted data points, DME-PC and DME-HC combine dynamically the ensemble member predictions based on their performance on recent data and predicted future performance. Experiments on four financial datasets for 10 years and comparison with statistical, machine learning and deep learning methods showed the superior performance of the proposed dynamic ensembles. The best results were obtained with DME-HC (EWA), which utilizes hierarchical clustering for ensemble member selection and exponentially weighted average on most recent errors. Our results highlight the potential of the proposed dynamic ensembles to effectively capture temporal patterns in financial data and support better investment decisions.