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Deep Learning Quantile Regression for Interval‐Valued Data Prediction

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  • Huiyuan Wang
  • Ruiyuan Cao

Abstract

Interval‐valued data are a special symbolic data, which contains rich information. The prediction of interval‐valued data is a challenging task. In terms of predicting interval‐valued data, machine learning algorithms typically consider mean regression, which is sensitive to outliers and may lead to unreliable results. As an important complement to mean regression, in this paper, a quantile regression artificial neural network based on a center and radius method (QRANN‐CR) is proposed to address this problem. Numerical studies have been conducted to evaluate the proposed method, comparing with several traditional models, including the interval‐valued quantile regression, the center method, the MinMax method, and the bivariate center and radius method. The simulation results demonstrate that the proposed QRANN‐CR model is an effective tool for predicting interval‐valued data with higher accuracy and is more robust than the other methods. A real data analysis is provided to illustrate the application of QRANN‐CR.

Suggested Citation

  • Huiyuan Wang & Ruiyuan Cao, 2025. "Deep Learning Quantile Regression for Interval‐Valued Data Prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(5), pages 1806-1825, August.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:5:p:1806-1825
    DOI: 10.1002/for.3271
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    References listed on IDEAS

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