Journal of Zhejiang University-SCIENCE A

, Volume 4, Issue 5, pp 578–583 | Cite as

Application of uncertainty reasoning based on cloud model in time series prediction

  • Zhang Jin-chun
  • Hu Gu-yu
Mechanics & Control Technology

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.

Key words

Time series prediction Cloud model Simple exponential smoothing method 

Document code

CLC number

TP393 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andrei, S. M., 1972. Weather Forecasting as a Problem in Physics. MIT press, Cambridge, MA1972.Google Scholar
  2. Dorffner, G., 1996. Neural networks for time series processing.Neural Network World,4: 447–468.Google Scholar
  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.Google Scholar
  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.CrossRefMATHGoogle Scholar
  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.Google Scholar
  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 (CI-FEr). Piscataway, NJ: 253–259.Google Scholar
  7. Li, D. Y., 1997a. Knowledge Representation and Discovery Based on Linguistic Atoms. Proceedings of the 1st Pacific-Asia Conference, Singapore, p. 3-20.Google Scholar
  8. Li, D. Y., 1997b. Knowledge representation in KDD based on linguistic atoms.Journal of Computer Science and Technology,12(6): 1–16.MathSciNetCrossRefGoogle Scholar
  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.Google Scholar
  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.Google Scholar
  11. Schwartz, M., 1998. Boardband Integrated Networks. Tsinghua University Press, China.Google Scholar

Copyright information

© Zhejiang University Press 2003

Authors and Affiliations

  • Zhang Jin-chun
    • 1
  • Hu Gu-yu
    • 1
  1. 1.Institute of Command AutomationPLA University of Science and TechnologyNanjingChina

Personalised recommendations