IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v44y2025i5p1806-1825.html

Deep Learning Quantile Regression for Interval‐Valued Data Prediction

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3271
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.3271?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yuying Sun & Kenan Qiao & Shouyang Wang, 2021. "Uncertainty shocks of Trump election in an interval model of stock market," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 865-879, May.
    2. Tao Xiong & Yukun Bao & Zhongyi Hu, 2014. "Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting," Papers 1401.1916, arXiv.org.
    3. Wooyoung Shin & Yoonsuh Jung, 2023. "Deep support vector quantile regression with non-crossing constraints," Computational Statistics, Springer, vol. 38(4), pages 1947-1976, December.
    4. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    5. Jia, Yichen & Jeong, Jong-Hyeon, 2022. "Deep learning for quantile regression under right censoring: DeepQuantreg," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    6. Fan, Yanqin & Shi, Xuetao, 2023. "Uniform Inference In A Generalized Interval Arithmetic Center And Range Linear Model," Econometric Theory, Cambridge University Press, vol. 39(1), pages 27-69, February.
    7. Dias, Sónia & Brito, Paula, 2017. "Off the beaten track: A new linear model for interval data," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1118-1130.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sun Mingran & Sun Yuying, 2026. "Forecasting the Conditional Distribution of Interval‐Valued Crude Oil Prices Using a Diffusion‐Based Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 470-495, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Yuying & Zhang, Xinyu & Wan, Alan T.K. & Wang, Shouyang, 2022. "Model averaging for interval-valued data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 772-784.
    2. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    3. Sun Mingran & Sun Yuying, 2026. "Forecasting the Conditional Distribution of Interval‐Valued Crude Oil Prices Using a Diffusion‐Based Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 470-495, March.
    4. Cheng, Zishu & Li, Mingchen & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024. "Climate change and crude oil prices: An interval forecast model with interval-valued textual data," Energy Economics, Elsevier, vol. 134(C).
    5. Feng Gao & Jie Song & Xueyan Shao, 2025. "Short-term interval-valued load forecasting with a combined strategy of iHW and multioutput machine learning," Annals of Operations Research, Springer, vol. 346(3), pages 2009-2033, March.
    6. Zhu, Yichen & Ghoshray, Atanu, 2021. "Climate Anomalies and Its Impact on U.S. Corn and Soybean Prices," 2021 Conference, August 17-31, 2021, Virtual 315271, International Association of Agricultural Economists.
    7. Haowen Bao & Yongmiao Hong & Yuying Sun & Shouyang Wang, 2024. "Sparse Interval-valued Time Series Modeling with Machine Learning," Papers 2411.09452, arXiv.org.
    8. Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
    9. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).
    10. Rui Luo & Jinpei Liu & Piao Wang & Zhifu Tao & Huayou Chen, 2024. "A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 366-390, March.
    11. Tao XIONG & LI Chongguang & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    12. Zaji, Amir Hossein & Bonakdari, Hossein & Khodashenas, Saeed Reza & Shamshirband, Shahaboddin, 2016. "Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 14-19.
    13. Zhang, Xinran & Yuan, Xiaohui & Wang, Chunjie & Song, Xinyuan, 2025. "Monotone composite quantile regression neural network for censored data with a cure fraction," Computational Statistics & Data Analysis, Elsevier, vol. 211(C).
    14. Yichen Jia & Jong-Hyeon Jeong, 2025. "DeepCENT: prediction of censored event time via deep learning," Computational Statistics, Springer, vol. 40(8), pages 4589-4605, November.
    15. Pradeep, Siddhartha, 2022. "Impact of diesel price reforms on asymmetricity of oil price pass-through to inflation: Indian perspective," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    16. Yang, Kun & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024. "Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach," Energy Economics, Elsevier, vol. 139(C).
    17. Ha, Youngmin & Zhang, Hai, 2019. "Fast multi-output relevance vector regression," Economic Modelling, Elsevier, vol. 81(C), pages 217-230.
    18. Wu, Dan & Dai, Xingyu & Zhao, Ruikun & Cao, Yaru & Wang, Qunwei, 2023. "Pass-through from temperature intervals to China's commodity futures’ interval-valued returns: Evidence from the varying-coefficient ITS model," Finance Research Letters, Elsevier, vol. 58(PA).
    19. Chuanmiao Yan & Xinyu Zhang & Ruhong Cui & Yuying Sun & Shouyang Wang, 2026. "DKformer: A Novel Transformer‐Based Model for Interval‐Valued Crude Oil Price Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 1020-1035, April.
    20. Teresa Perry, 2023. "Did the 2016 election cause changes in substance use? An intersectional approach," Economics and Politics, Wiley Blackwell, vol. 35(3), pages 1020-1069, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:44:y:2025:i:5:p:1806-1825. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.