Skip to main content

Intelligent Monitoring Method Using Time Varying Binomial Distribution Models for Pseudo-Periodic Communication Traffic

  • Conference paper
  • First Online:
Advances in Intelligent Data Analysis (IDA 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1642))

Included in the following conference series:

Abstract

In this paper, we deal with the degradation detection problem for telecommunication network gateways. The time series to be monitored is non-stationary but almost periodic (pseudo-periodic). The authors propose a technique called “optimization of partition model” which generates local stationary models for a pseudo-periodic time series. The optimization is based on the minimal AIC principle. The technique called SPRT is also applied to make efficient decisions. Experiments to evaluate methods for optimization, incremental model update, and the comparison with the conventional method are conducted with real data. The result shows the proposed method is effective and makes more precises decision than the conventional one.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. ITU: Telephone Network and ISDN-Operation, Numbering, Routing and Mobile Service-. Rec. E.401-E.427, BLUE BOOK, Volume II, FascicleII.3, 1989.

    Google Scholar 

  2. Wald, A.: Sequential Analysis, Wiley, New York, 1947.

    MATH  Google Scholar 

  3. Gertler, J.: Survey of Model-Based Failure Detection and Isolation in Complex Plants. IEEE Control Systems Magazine, Dec. (1988) 3–11

    Google Scholar 

  4. Akaike, H.: A New Look at Statistical Model Identification. IEEE Transactions on Automatic Control, 19 (1974) 716–723

    Article  MATH  MathSciNet  Google Scholar 

  5. Chien, T. and M. B. Adams: A Sequential Failure Detection Technique and Its Application. IEEE Transactions on Automatic Control, Vol. AC-21 (1976) 750–757

    Article  Google Scholar 

  6. Takanami, T. and G. Kitagawa: A new Efficient procedure for the estimation of onset times of seismic waves. Journal of Physics of the Earth, Vol.36, (1988) 267–290.

    Google Scholar 

  7. Uosaki, K.: Failure Detection Using Backward SPRT. Proceedings of IFAC Symposium on Identifications and System Parameter Estimation, York, UK, (1985) 1619–1624

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Matsumoto, K., Hashimoto, K. (1999). Intelligent Monitoring Method Using Time Varying Binomial Distribution Models for Pseudo-Periodic Communication Traffic. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_11

Download citation

  • DOI: https://doi.org/10.1007/3-540-48412-4_11

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66332-4

  • Online ISBN: 978-3-540-48412-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics