An application of artificial neural network to predict the added value of oil, gas and petroleum industry products

Abstract

This paper presents an empirical investigation on fluctuation and trend of added value changes in oil and gas industries and their products and also to anticipate the current added value of these industries. For this aim, fluctuation of added values of different subsidiaries such as oil group products, exporting crude oil, production of gas and petroleum is investigated over the period 1959-2004. The study selects the best network to anticipate added value in subsidiaries of energy section. For the training and testing the network, all data are divided into two groups. To define input layer neurons number which are equal to auto regressive vector rank in ARMA method, the rank of auto regressive (p) and mobile mean (q) have been used according to proposed method of Pesaran & Pesaran. The simulated results have been extracted by using neural networks, in feed forward network which had low compatibility with the real added value.

Keywords

• Oil and Gas industry
• Neural network
• Forecasting added value