Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models

Hossein Hassani, Emmanuel Sirimal Silva, Rangan Gupta and Sonali Das ()

Physica A: Statistical Mechanics and its Applications, 2018, vol. 509, issue C, 121-139

Abstract: In this paper we analyse whether (anthropometric) CO2 can forecast global temperature anomaly (GT) over an annual out-of-sample period of 1907–2012, which corresponds to an initial in-sample of 1880–1906. For our purpose, we use 12 parametric and nonparametric univariate (of GT only) and multivariate (including both GT and CO2) models. Our results show that the Horizontal Multivariate Singular Spectral Analysis (HMSSA) techniques (both Recurrent (-R) and Vector (-V)) consistently outperform the other competing models. More importantly, from the performance of the HMSSA-V model we find conclusive evidence that CO2 can forecast GT, and also predict its direction of change. Our results highlight the superiority of the nonparametric approach of SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.

Keywords: CO2 emissions; Forecasting; Global temperature anomaly; Univariate and multivariate models (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 Q53 Q54 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Related works:
Working Paper: Predicting Global Temperature Anomaly: A Definitive Investigation Using an Ensemble of Twelve Competing Forecasting Models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:509:y:2018:i:c:p:121-139

DOI: 10.1016/j.physa.2018.05.147

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