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A Unique Expert System For Optimum Oil Price Estimation By Integration Of Fuzzy Cognitive Map, Neural Networks And Ga

1145 words - 5 pages

Crude oil plays an important role in any economies. The role of oil in the world economy becomes more and more significant, because nearly two-thirds of the world’s energy consumption comes from the crude oil and natural gas. The crude oil price is basically determined by its supply and demand and it is strongly influenced by many events like the weather, inventory, GDP growth, refinery operable capacity, political aspects and people’s expectation. Sharp oil price movements are likely to disturb aggregate economic activity and volatile oil prices have been considerable interest to many researchers and institutions. Therefore, forecasting oil prices is an important and very hard topic due to ...view middle of the document...

Yousefi et al. (2005) introduced a wavelet-based prediction procedure and market data on crude oil to provide forecasts over different forecasting horizons. Sadorsky (2006) used several different univariate and multivariate statistical models such as TGARCH and GARCH to forecast daily volatility in petroleum future price returns. Kulkarni and Haidar (2009) used a model based on multilayer feed forward neural network to forecast crude oil spot price direction in the short-term. Several data preprocessing methods were tested by them. Manera et al. (2007) compared the performance of several static and dynamic forecasting models. They proposed a new class of models that combines the relevant aspects of the financial and structural specifications proposed in the literature. Nochai and Nochai (2006) considered forecasting of oil palm price of Thailand in three types as farm price, wholesale price and pure oil price. They found an appropriate ARIMA model that minimizes mean absolute percentage error (MAPE). Coppola (2008) evaluated the long-run relationship between spot and future prices. He forecasted out of sample oil spot and future price movements using vector error correction model (VECM). Azadeh et al. (2012) used artificial neural network (ANN) and fuzzy regression (FR) to forecast long-term oil price in noisy, uncertain, and complex environments. The oil supply, crude oil distillation capacity, oil consumption of non-OECD, USA refinery capacity, and surplus capacity are incorporated as the economic indicators. Liang et al. (2005) proposed an approach based on wavelet multi-scale function to predict the future long-term trend of oil price according to the oil price time series. Results show the proposed approach outperforms some other time series prediction approaches such as ARIMA, GARCH, and Hot-Winters. Ye et al. (2005) proposed a short-term forecasting model based on readily OECD industrial petroleum inventory levels to forecast monthly West Texas Intermediate crude oil spot prices. Liu and Wan (2012) considered dynamics of daily volatility of Shanghai fuel oil future prices using realized volatility (RV) and GARCH-class models. They found that RV models for intraday data outperform GARCH-class models for daily data in forecasting fuel oil price volatility. Yu et al. (2008) proposed empirical mode decomposition (EDM) based neural network ensemble learning model for world crude oil spot price modeling and forecasting. They used West Texas Intermediate (WTI) crude oil spot price and Brent...

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