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The volatility of crude oil market and its chain effects to the world economy augmented the interest and fear of individuals, public and private sectors. Previous statistical and econometric techniques used for prediction, offer good results when dealing with linear data. Nevertheless, crude oil price series deal with high nonlinearity and irregular events. The continuous usage of statistical and...
The fluctuation of oil prices attracts the great attention of the world. However, the prediction of oil prices is very difficult because the oil price system is so complex. In this paper, AR-GMDH algorithm and AC algorithm are adopted to forecast oil prices. The validity and feasibility of self-organizing data mining are manifested by the comparisons of the prediction result with that of conventional...
In this paper, the efficiency of neuro fuzzy network (ANFIS) is examined against auto regression (AR). Mean absolute percentage error (MAPE) is applied for this purpose. After applying different data preprocessing methods, the models are developed. A method for calculating ANFIS performance is also proposed. Due to various seasonal and monthly changes in oil production and difficulties in modeling...
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