International Journal of Mathematical Sciences and Computing(IJMSC)

ISSN: 2310-9025 (Print), ISSN: 2310-9033 (Online)

Published By: MECS Press

IJMSC Vol.6, No.5, Oct. 2020

Forecasting Natural Gas Prices Using Nonlinear Autoregressive Neural Network

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Abdelkader Sahed, Mohammed Mékidiche, Hacen Kahoui

Index Terms

Forecasting, ARIMA, NAR, Natural Gas Price.


When forecasting time series, It was found that simple linear time series models usually leave facets of economic and financial unknown in the forecasting time series due to linearity behavior, which remains the focus of empirical and applied study. The study suggested the Nonlinear Autoregressive Neural Network model and a comparison was made using the ARIMA model for forecasting natural gas prices, as obtained from the analysis, NAR models were better than the completed ARIMA model, measured against three performance indicators. The decision criterion for the selection of the best suited model depends on MSE, RMSE and R2. From the results of the criterion it has found that both the models are providing almost closed results but NAR is the best suited model for the forecasting of natural gas prices.

Cite This Paper

Abdelkader Sahed, Mohammed Mékidiche, Hacen Kahoui. " Forecasting Natural Gas Prices Using Nonlinear Autoregressive Neural Network ", International Journal of Mathematical Sciences and Computing (IJMSC), Vol.6, No.5, pp.37-46, 2020. DOI: 10.5815/ijMSC.2020.05.04


[1] Al-Fattah, S. M., and R. A. (2000). Startzman. Forecasting world natural gas supply. Journal of petroleum technology 52.05: 62-72.

[2] Li, J., Dong, X., Shangguan, J., & Hook, M. (2011). Forecasting the growth of China’s natural gas consumption. Energy, 36(3), 1380-1385.

[3] Szoplik, J. (2015). Forecasting of natural gas consumption with artificial neural networks. Energy, 85, 208-220.

[4] Kaboudan, M. A., & Liu, Q. W. (2004). Forecasting quarterly US demand for natural gas. Information Technology for Economics and Management, 2(1).

[5] Hosseinipoor, S. (2016). Forecasting Natural Gas Prices in the United States Using Artificial Neural Networks. MASTER OF SCIENCE IN NATURAL GAS ENGINEERING AND MANAGEMENT, UNIVERSITY OF OKLAHOMA.

[6] Hosseinipoor, S., Hajirezaie, S., & Nejati, J. (2016). Application of ARIMA and GARCH Models in Forecasting the Natural Gas Prices. Student J. OKLAHOMA Univ.PP.1-16.

[7] Siddiqui, A. W. (2019, May). Predicting Natural Gas Spot Prices Using Artificial Neural Network. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS) IEEE. PP. 1-6.

[8] Čeperić, E., Žiković, S., & Čeperić, V. (2017). Short-term forecasting of natural gas prices using machine learning and feature selection algorithms. Energy, 140, 893-900.

[9] Azadeh, A., Sheikhalishahi, M., & Shahmiri, S. (2012, March). A hybrid neuro-fuzzy approach for improvement of natural gas price forecasting in vague and noisy environments: domestic and industrial sectors. In Proceedings of the International Conference on Trends in Industrial and Mechanical Engineering, Dubai, UAE (pp. 24-25).

[10] Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014, 1-7.

[11] Zhang, Y., Yang, H., Cui, H., & Chen, Q. (2019). Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China. Natural Resources Research, 1-18.

[12] Wang, K. W., Deng, C., Li, J. P., Zhang, Y. Y., Li, X. Y., & Wu, M. C. (2017). Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiology & Infection, 145(6), 1118-1129.

[13] Beale, M. H., Hagan, M. T., & Demuth, H. B. (2016). Neural network toolbox™ getting started guide. Natick: The MathWorks.

[14] Ruiz, L. G. B., Cuéllar, M. P., Calvo-Flores, M. D., & Jiménez, M. D. C. P. (2016). An application of non-linear autoregressive neural networks to predict energy consumption in public buildings. Energies, 9(9), 684.

[15] Markova, M. (2019, October). Foreign exchange rate forecasting by artificial neural networks. In AIP Conference Proceedings (Vol. 2164, No. 1, p. 060010). AIP Publishing LLC. 1-15.

[16] Le, T. T., Pham, B. T., Ly, H. B., Shirzadi, A., & Le, L. M. (2020). Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network. In CIGOS 2019, Innovation for Sustainable Infrastructure (pp. 1191-1196). Springer, Singapore.