A Bayesian Dynamic Forecast Model Based On Neural Network

被引：0
|
作者：
Song Chaohong [1 ]
Luo Qiang [2 ]
Shi feng [1 ]
机构：
[1] Huazhong Agr Univ, Dept Informat & Comp Sci, Wuhan 430070, Hubei, Peoples R China
[2] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
来源：
2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION WORKSHOP: IITA 2008 WORKSHOPS, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/IITA.Workshops.2008.57
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A neural network-based Bayesian dynamic forecasting model is provided in this paper. Compared with the traditional Bayesian forecasting model, the given model can also has the virtues such as it does not need the placidity suppose which is necessary in the traditional time series forecast method and it can obtain more accurate estimate even with few datum depended on the subjective priori information. In additional, the given model can also improve forecast precision for unexpected events by takes the prediction of neural network as specialist's information. At last, the given model is used to forecast water supply of Shenzhen. Case study showed that the given model could enhance the forecast precision. The forecasting result is much better than that of the forecast of grey and neural network.
引用
收藏
页码：130 / +
页数：2
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