A PREDICTIVE ALGORITHM BASED ON NEURAL NETWORK MODEL FOR TIMES SERIES AND ITS APPLICATION

This paper applies a hybrid learning algorithm based on neural network model to predict time series with several steps in advance. The proposed algorithm combines time difference method with BP algorithm with forgetting. It helps to solve the computing problem incrementally in traditional BP algorithm on multi-step predicting and has the ability of structural learning. The predictions of the silicon content time series dota of the hot metal in blast furnace show that the method proposed here is feasible.