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

Building on the insights from Bonas et al. (2024), we explore the relationship between statistical and machine learning models in the analysis of environmental time series. We specifically address the unique challenges of environmental time series data, including the need to consider the multivariate approach and account for spatial dependence. Emphasizing the importance of various types of statistical inference in environmental studies—not limited to forecasting—we propose that viewing statistical and machine learning approaches as complementary rather than alternative methods can unlock innovative modeling strategies that enhance both predictive accuracy and interpretive power. To illustrate these concepts, we present a case study that highlights the key points raised in the discussion.

• Publication year

  2025

• Venue

  Environmetrics

• Publication date

  2025-02-05

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1002/env.2900
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Assessing predictability of environmental time series with statistical and machine learning models

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