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Exploring hybrid models for forecasting \(CO_2\) emissions in drought-prone Somalia: a comparative analysis

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Abstract

Climate change poses significant challenges globally, demanding accurate forecasting methodologies to comprehend and address its consequences. This paper presents a comparative analysis of hybrid models for forecasting carbon dioxide (\(CO_2\)) emissions in drought-prone Somalia. The study employs 15 time series models, including six single time series models AutoRegressive Integrated Moving Average (ARIMA), Error Trend Seasonality (ETS), Trigonometric seasonality, Box-Cox transformation (TBATS), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), and Neural Network AutoRegression (NNAR) and nine hybrid models (ARIMA-ETS, ARIMA-TBATS, ARIMA-Theta, ARIMA-NNAR, ARIMA-ETS-TBATS, ARIMA-NNAR-Theta, ARIMA-NNAR-TBATS, ARIMA-ETS-Theta, and ARIMA-ETS-NNAR). The dataset spans from 1950 to 2020, with a training set from 1950 to 2010 and a testing/validation set from 2011 to 2020. The models are evaluated using various metrics, including Mean Absolute Scaled Error (MASE), Symmetric Mean Absolute Percentage Error (SMAPE), Mean Absolute Percentage Error (MAPE), and Theil’s U statistic. The data are found to be non-stationary, requiring the application of differencing techniques. Among the single time series models, TBATS(0.717, 0,0, 1, -) performs the best, while the ARIMA-TBATS hybrid model outperforms the other hybrid models. The forecasts provide valuable insights for addressing the challenges posed by charcoal burning and exporting in Somalia. The study concludes with recommendations for future research, including the incorporation of socio-economic and policy variables, spatial analysis, and long-term forecasting.

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Data Availability

The data that support the findings of this study are available on request from the corresponding author.

Abbreviations

ADF:

Augmented Dickey-Fuller

AIC:

Akaike Information Criterion

ARFIMA:

Autoregressive Fractionally Integrated Moving Average

ARIMA:

Autoregressive Integrated Moving Average

ETS:

Error Trend Seasonality

MASE:

Mean Absolute Scaled Error

MAPE:

Mean Absolute Percentage Error

NNAR:

Neural Network Autoregression

PP:

Phillips-Perron

SMAPE:

Symmetric Mean Absolute Percentage Error

TBATS:

Trigonometric Seasonal Decomposition of Time Series

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Funding

This research received no external funding.

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Author notes

  1. Omer Mohamed Egeh, Christophe Chesneau and Abdisalam Hassan Muse contributed equally to this work.

Authors and Affiliations

  1. School of Postgraduate Studies and Research, Amoud University, Amoud Valley, Borama, 25263, Awdal, Somalia

    Omer Mohamed Egeh & Abdisalam Hassan Muse

  2. Department of Mathematics, LMNO, CNRS-Université de Caen, Campus II, Science 3, 14032, Caen, France

    Christophe Chesneau

Authors
  1. Omer Mohamed Egeh
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  2. Christophe Chesneau
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  3. Abdisalam Hassan Muse
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Contributions

O.M.E., C.C. and A.H.M. conceived the research topic, explored that idea, performed the analysis and drafted the manuscript.

Corresponding author

Correspondence to Christophe Chesneau.

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The authors declare no conflict of interest.

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Communicated by: H. Babaie.

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Egeh, O.M., Chesneau, C. & Muse, A.H. Exploring hybrid models for forecasting \(CO_2\) emissions in drought-prone Somalia: a comparative analysis. Earth Sci Inform 16, 3895–3912 (2023). https://doi.org/10.1007/s12145-023-01126-0

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