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Predicting Global Temperature Anomaly: A Definitive Investigation Using an Ensemble of Twelve Competing Forecasting Models

Author

Listed:
  • Hossein Hassani

    (Statistical Research Centre, The Business School, Bournemouth University, UK)

  • Emmanuel Sirimal Silva

    (Statistical Research Centre, The Business School, Bournemouth University, UK)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Sonali Das

    (Council for Scientific and Industrial Research, P.O. Box 395, Pretoria, 0001, South Africa and Department of Statistics, PO Box 77000, Nelson Mandela Metropolitan University, Port Elizabeth, 6031, South Africa)

Abstract

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

Suggested Citation

  • Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Sonali Das, 2015. "Predicting Global Temperature Anomaly: A Definitive Investigation Using an Ensemble of Twelve Competing Forecasting Models," Working Papers 201561, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201561
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    Cited by:

    1. Hassani, Hossein & Silva, Emmanuel Sirimal, 2018. "Forecasting UK consumer price inflation using inflation forecasts," Research in Economics, Elsevier, vol. 72(3), pages 367-378.
    2. Jenny Cifuentes & Geovanny Marulanda & Antonio Bello & Javier Reneses, 2020. "Air Temperature Forecasting Using Machine Learning Techniques: A Review," Energies, MDPI, vol. 13(16), pages 1-28, August.
    3. Mohammad Reza Yeganegi & Hossein Hassani & Rangan Gupta, 2023. "The ENSO cycle and forecastability of global inflation and output growth: Evidence from standard and mixed‐frequency multivariate singular spectrum analyses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1690-1707, November.
    4. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    5. Alexandra M. Schmidt & Marco A. Rodríguez, 2022. "Discussion on “A combined estimate of global temperature”," Environmetrics, John Wiley & Sons, Ltd., vol. 33(3), May.
    6. Hassani, Hossein & Huang, Xu & Gupta, Rangan & Ghodsi, Mansi, 2016. "Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 54-65.
    7. Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).
    8. Silva, Emmanuel Sirimal & Hassani, Hossein & Heravi, Saeed & Huang, Xu, 2019. "Forecasting tourism demand with denoised neural networks," Annals of Tourism Research, Elsevier, vol. 74(C), pages 134-154.
    9. Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021. "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, vol. 14(11), pages 1-21, May.

    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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