Work

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

Year: 2025

Type: article

Abstract: ABSTRACT 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.

Source: Environmetrics

DOI: 10.1002/env.2900

Authors: Francesco Finazzi, Jacopo Rodeschini, Lorenzo Tedesco

Institution: University of Bergamo

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,780

Funder: European Commission