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

Hossein Hassani, Emmanuel Sirimal Silva*, Rangan Gupta, Sonali Das

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)121-139
Number of pages19
JournalPhysica A: Statistical Mechanics and its Applications
Volume509
DOIs
Publication statusPublished - 1 Nov 2018
Externally publishedYes

Keywords

  • CO emissions
  • Forecasting
  • Global temperature anomaly
  • Univariate and multivariate models

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability

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