International Journal of Mathematical Sciences and Computing (IJMSC)
IJMSC Vol. 6, No. 5, 8 Oct. 2020
Cover page and Table of Contents: PDF (size: 328KB)
Forecasting Natural Gas Prices Using Nonlinear Autoregressive Neural Network
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Author(s)
Index Terms
Forecasting, ARIMA, NAR, Natural Gas Price.
Abstract
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
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