Abstract
This paper has discussed the possibility and key problem to construct the neural network time series model, and three time series neural network forecasting methods has been proposed, i. e. a neural network nonlinear time series model, a neural network multi-dimension time series model and a neural network combining predictive model. These three methods are applied to real problems. The results show that these methods are better than the traditional one. Furthermore, the neural network methods are compared with the traditional method, and the constructed model of intellectual information forecasting system is given.
Similar content being viewed by others
References
Wong Wenbo, The Base of Forecasting Theory, Petroleum Industry Publishing Co., Beijing, 1984.
T. Poggio, F. Giros, Networks for Approximation and Learning,Proc. IEEE,78 (1990), 1481–1497.
R. Hicht-Nielsen, Theory of the back propagation neural network, Proceedings of the International Conference on Neural Networks, San Diego: SOS Printing, 1 (1987), 593–608.
A. K. Kolmogorov, On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition,Doklady Akademii Nauk SSR,114 (1957), 953–956.
J. M. Bates, C. W. J. Graner, The combination of forecasts,Operations Research Quarterly,20 (1969) 2, 319–325.
Author information
Authors and Affiliations
About this article
Cite this article
Xinhui, W., Kaizhou, C. Time series neural network forecasting methods. J. of Electron. (China) 12, 1–8 (1995). https://doi.org/10.1007/BF02684561
Issue Date:
DOI: https://doi.org/10.1007/BF02684561