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Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

Author

Listed:
  • Palanisamy Manigandan

    (Department of Statistics, Periyar University, Salem P.O. Box 636011, Tamil Nadu, India)

  • MD Shabbir Alam

    (Department of Economics & Finance, College of Business Administration, University of Bahrain, Zallaq P.O. Box 2038, Bahrain)

  • Majed Alharthi

    (Finance Department, College of Business, King Abdulaziz University, Rabigh 21911, Saudi Arabia)

  • Uzma Khan

    (Department of Finance, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Kuppusamy Alagirisamy

    (Department of Statistics, Periyar University, Salem P.O. Box 636011, Tamil Nadu, India)

  • Duraisamy Pachiyappan

    (Department of Statistics, Periyar University, Salem P.O. Box 636011, Tamil Nadu, India)

  • Abdul Rehman

    (College of Economics and Management, Henan Agricultural University, Zhengzhou 450002, China)

Abstract

Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA ( p , d , q ) * ( P , D , Q ) s . The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.

Suggested Citation

  • Palanisamy Manigandan & MD Shabbir Alam & Majed Alharthi & Uzma Khan & Kuppusamy Alagirisamy & Duraisamy Pachiyappan & Abdul Rehman, 2021. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6021-:d:640419
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    as
    1. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
    2. G. E. P. Box & G. M. Jenkins & J. F. MacGregor, 1974. "Some Recent Advances in Forecasting and Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(2), pages 158-179, June.
    3. Bierens, Herman J., 1987. "Armax model specification testing, with an application to unemployment in the Netherlands," Journal of Econometrics, Elsevier, vol. 35(1), pages 161-190, May.
    4. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
    5. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    6. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    7. Tarsitano, Agostino & Amerise, Ilaria L., 2017. "Short-term load forecasting using a two-stage sarimax model," Energy, Elsevier, vol. 133(C), pages 108-114.
    8. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, vol. 24(4), pages 659-678.
    9. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    10. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    11. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    12. Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
    13. Deetman, Sebastiaan & Hof, Andries F. & Pfluger, Benjamin & van Vuuren, Detlef P. & Girod, Bastien & van Ruijven, Bas J., 2013. "Deep greenhouse gas emission reductions in Europe: Exploring different options," Energy Policy, Elsevier, vol. 55(C), pages 152-164.
    14. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    15. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    16. Kinateder, Harald & Campbell, Ross & Choudhury, Tonmoy, 2021. "Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets," Finance Research Letters, Elsevier, vol. 43(C).
    17. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    18. Chen, Ying & Xu, Xiuqin & Koch, Thorsten, 2020. "Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model," Applied Energy, Elsevier, vol. 262(C).
    19. Perron, Pierre, 1990. "Testing for a Unit Root in a Time Series with a Changing Mean," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 153-162, April.
    20. Karadede, Yusuf & Ozdemir, Gultekin & Aydemir, Erdal, 2017. "Breeder hybrid algorithm approach for natural gas demand forecasting model," Energy, Elsevier, vol. 141(C), pages 1269-1284.
    21. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    22. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    23. Karakurt, Izzet, 2021. "Modelling and forecasting the oil consumptions of the BRICS-T countries," Energy, Elsevier, vol. 220(C).
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    2. Shahenaz Mulla & Chaitanya B. Pande & Sudhir K. Singh, 2024. "Times Series Forecasting of Monthly Rainfall using Seasonal Auto Regressive Integrated Moving Average with EXogenous Variables (SARIMAX) Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 1825-1846, April.
    3. Sebastian Majewski & Urszula Mentel & Raufhon Salahodjaev & Marek Cierpiał-Wolan, 2022. "Electricity Consumption and Economic Growth: Evidence from South Asian Countries," Energies, MDPI, vol. 15(4), pages 1-10, February.
    4. Duraisamy Pachiyappan & Yasmeen Ansari & Md Shabbir Alam & Prabha Thoudam & Kuppusamy Alagirisamy & Palanisamy Manigandan, 2021. "Short and Long-Run Causal Effects of CO 2 Emissions, Energy Use, GDP and Population Growth: Evidence from India Using the ARDL and VECM Approaches," Energies, MDPI, vol. 14(24), pages 1-17, December.

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