Document Type: Original Article
PhD Student in Oil and Gas Economics, Faculty of Economics, Allameh Tabataba`i University, Tehran, Iran
Assistant Professor, Faculty of Economics, Allameh Tabataba`i University, Tehran, Iran
Associate Professor of Energy Economics, Faculty of Economics, Allameh Tabataba`i University, Tehran, Iran
The liberalization of natural gas markets and the emergence of gas hubs in recent decades have shifted the natural gas trade from the regional to the global trade. The growth and maturity of these hubs have weakened the previously established relationship between the natural gas price and the prices of crude oil and petroleum products. Therefore, predicting the price of gas as a strategic commodity has become more important for different countries. Using the neural network method, this paper attempts to provide a model for the monthly prediction of natural gas price. Based on the time series data from 2012 to April 2019 as neural network input, this model predicts the prices in five hubs and natural gas exchange centers in Europe. Based on the R2 performance evaluation index of 98% in the neural network model fitted based on the aforementioned data series, the neural network model has an acceptable performance in predicting the natural gas price. The results of this study show that using the artificial neural network (ANN) method, the gas prices in the European gas hubs, which are located in European Country, can be predicted with a high accuracy.