Clustering Methods for Identifying and Modelling Areas with Similar Temperature Variations

TL;DR

This study introduces a data-driven clustering approach combined with Space-Time AutoRegressive models to better understand and predict global temperature variations, outperforming traditional methods.

Contribution

It presents a novel combination of clustering and STAR models using statistical similarity measures, enhancing temperature variation modelling accuracy.

Findings

Distance-based STAR models outperform classical models.

Hamming distance-based STAR model achieves highest predictive accuracy.

Statistical similarity improves global temperature modelling.

Abstract

This paper proposes a novel data-driven approach for identifying and modelling areas with similar temperature variations throufigureh clustering and Space-Time AutoRegressive (STAR) models. Using annual temperature data from 168 countries (1901-2022), we apply three clustering methods based on (i) warming rates, (ii) annual temperature variations, and (iii) persistence of variation signs, using Euclidean and Hamming distances. These clusters are then employed to construct alternative spatial weight matrices for STAR models. Empirical results show that distance-based STAR models outperform classical contiguity-based ones, both in-sample and out-of-sample, with the Hamming distance-based STAR model achieving the best predictive accuracy. The study demonstrates that using statistical similarity rather than geographical proximity improves the modelling of global temperature dynamics, suggesting broader applicability to other environmental and socioeconomic datasets.