Full Text:
<2546>
CLC number: TP393
On-line Access:
Received: 2002-09-03
Revision Accepted: 2003-01-08
Crosschecked: 0000-00-00
Cited: 3
Clicked: 5626
Application of uncertainty reasoning based on cloud model in time series prediction
| |||||||||||||
ZHANG Jin-chun, HU Gu-yu. Application of uncertainty reasoning based on cloud model in time series prediction[J]. Journal of Zhejiang University Science A, 2003, 4(5): 578-583.
@article{title="Application of uncertainty reasoning based on cloud model in time series prediction",
author="ZHANG Jin-chun, HU Gu-yu",
journal="Journal of Zhejiang University Science A",
volume="4",
number="5",
pages="578-583",
year="2003",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2003.0578"
}
%0 Journal Article
%T Application of uncertainty reasoning based on cloud model in time series prediction
%A ZHANG Jin-chun
%A HU Gu-yu
%J Journal of Zhejiang University SCIENCE A
%V 4
%N 5
%P 578-583
%@ 1869-1951
%D 2003
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2003.0578
TY - JOUR
T1 - Application of uncertainty reasoning based on cloud model in time series prediction
A1 - ZHANG Jin-chun
A1 - HU Gu-yu
J0 - Journal of Zhejiang University Science A
VL - 4
IS - 5
SP - 578
EP - 583
%@ 1869-1951
Y1 - 2003
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2003.0578
Abstract: time series prediction has been successfully used in several application areas, such as meteorological forecasting, market prediction, network traffic forecasting, etc., and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.
[1]Andrei,S.M., 1972. Weather Forecasting as a Problem in Physics. MIT press, Cambridge, MA1972.
[2]Dorffner,G., 1996. Neural networks for time series processing. Neural Network World, 4: 447-468.
[3]Edwards,T., Tansley,D.S.W., Frank,R.J. and Davey,N., 1997. Traffic trends analysis using neural networks. Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications, 3: 157-164.
[4]Frank,R.J., Davey,N. and Hunt,S.P., 2001. Time series prediction and neural networks. Journal of intelligent and robotic systems, 31: 91-103.
[5]Gershenfeld,N.A. and Weigend,A.S., 1993. The Future of Time Series. Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley Pub. Co., Santa Fe, NM: 1-70.
[6]Giles,C.L., Lawewnce,S. and Tsoi,A.C., 1997. Rule Inference for Financial Prediction Using Recurrent Neural Networks. Proceedings of IEEE/IAFE conference on computational intelligence for financial engineering (CIFEr). Piscataway, NJ: 253-259.
[7]Li,D.Y., 1997a. Knowledge Representation and Discovery Based on Linguistic Atoms. Proceedings of the 1st Pacific-Asia Conference, Singapore, p.3-20.
[8]Li,D.Y., 1997b. Knowledge representation in KDD based on linguistic atoms. Journal of Computer Science and Technology, 12(6): 1-16.
[9]Li,D.Y., Di,K.C., Li,D.R. and Song,Z.L., 2000. Mining association rules with linguistic cloud models. Journal of Software, 11(2): 143-158.
[10]Ou,J.P. and Li,L.J., 1999. The application of ANN in short-term load prediction in power system. Guangdong Electric Power, 2: 27-31.
[11]Schwartz,M., 1998. Boardband Integrated Networks. Tsinghua University Press, China.
Open peer comments: Debate/Discuss/Question/Opinion
<1>