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In this paper we introduce a stochastic time strength RBF neural network (ST-RBF) to prices forecasting of crude oil indices in which the network is set up by taking into consideration the impact of occurrence time for the historic data. A stochastic time-effective function is utilized to depict this, and a weight is assigned to each historic data, in which the time strength behavior is expressed through a combination of a Brownian function and a drift function. In empirical experiment, the data adopted is Chinese Daqing crude oil and Brent crude oil which is marked as one of the main global crude oil benchmarks. It is shown that the improved ST-RBF outperforms the traditional RBF neural network and increases the precision in predicting of crude oil prices.
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