Chaos theory and predict future prices in the oil products

Today, special attention has been paid to the capabilities of chaos theories and neural networks and the application of these two models in financial markets, especially petroleum products. In this study, daily values ​​of stocks of Iranian petroleum products during December 2007 to June 2016 have been studied. Due to the nonlinear nature of financial data, chaos theory is used to study the chaotic amount of time series. The chaos theory based on Liapanov's exponent and fractal dimension studies the time series caused by nonlinear dynamic systems. In chaos theory, first, using the Lyapanov diagrams and the Poincaré surface of section map and measuring the correlation dimension, the possibility of chaos in time series of daily value of oil products in Iran is investigated. Then, the Lyapunov diagram is plotted using the delay time estimation obtained from the method of the average of mutual information and embedding dimension using the algorithm of the false nearest neighbors. The Lyapunov map and the Poincaré surface of section indicate a chaos in the investigated time series. According to the provocation of chaos in this time series, its nonlinearity was deduced. Therefore, a suitable neural network was designed and the best model was selected to predict the future prices of petroleum products stock with a correlation coefficient of 0.99831 and the error of the training data is 0.0012 and the error of the test data is 0.002 that indicating good accuracy in modeling the price of these industries and can be used to predict its future price.