IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v36y2025i2ne70001.html
   My bibliography  Save this article

Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”

Author

Listed:
  • Paolo Maranzano
  • Paul A. Parker

Abstract

We contribute to the discussion of the insightful article “Assessing predictability of environmental time series with statistical and machine learning models” by Bonas et al. (2024), in which the authors commend their effort in comparing a wide range of methodologies for the challenging task of predicting environmental time series data. We focus our discussion on two topics of interest to us. First, we consider extensions of the explored methodologies that allow for heteroscedastic error terms. Second, we consider non‐Gaussianity and fitting models on transformed data. For both of these points, we will make use of the authors' supplied code and data in order to extend their examples. Ultimately, we find that modeling of heteroscedasticity error terms has the potential to improve both point and interval estimates for these environmental time series. We also find that the use of transformations to handle non‐Gaussianity can improve interval estimates.

Suggested Citation

  • Paolo Maranzano & Paul A. Parker, 2025. "Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.
  • Handle: RePEc:wly:envmet:v:36:y:2025:i:2:n:e70001
    DOI: 10.1002/env.70001
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.70001
    Download Restriction: no

    File URL: https://libkey.io/10.1002/env.70001?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
    ---><---

    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:envmet:v:36:y:2025:i:2:n:e70001. 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.

    We have no bibliographic references for this item. You can help adding them by using 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://www.interscience.wiley.com/jpages/1180-4009/ .

    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.