Fuzzy logic coupled with exhaustive search algorithm for forecasting of petroleum economic parameters

Abstract In the upstream of oil and gas sector, many development plans and strategic decisions are based on economic calculations. Therefore, it is so important to establish novel models for accurate forecasting of economic variables with minimized error. In this study, two rule-based fuzzy inference systems based on Mamdani-type and TSK models coupled with exhaustive search algorithm are applied to forecast monthly oil price (MOP), daily gas price (DGP), and annually interest rate (AIR). All required data were collected from Central Bank of the Islamic Republic of Iran. In addition, autoregressive integrated moving average (ARIMA) model is used as a reasonable benchmark to identify whether the proposed fuzzy based methods can outperform ARIMA models for forecasting of economic factors. The statistical significance of results is evaluated using a bootstrap algorithm and various error criteria. The results show that Mamdani-type outperforms ARIMA and TSK in MOP, DGP, and AIR forecast. The results indicate that optimized Mamdani-type reduces RMSE by 69.19% MOP prediction, by 84.37% in DGP prediction, and by 93.67% in AIR prediction over benchmark method.

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