Intelligent monitoring method using time varying binomial distribution models for pseudo-periodic communication traffic

Kazunori Matsumoto, Kazuo Hashimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages123-134
Number of pages12
Volume1642
ISBN (Print)3540663320, 9783540663324
DOIs
Publication statusPublished - 1999
Externally publishedYes
Event3rd International Symposium on Intelligent Data Analysis, IDA 1999 - Amsterdam, Netherlands
Duration: 1999 Aug 91999 Aug 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1642
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Symposium on Intelligent Data Analysis, IDA 1999
CountryNetherlands
CityAmsterdam
Period99/8/999/8/11

Fingerprint

Binomial distribution
Telecommunication traffic
Time-varying
Traffic
Monitoring
Time series
Sequential Probability Ratio Test
Telecommunication Network
Optimization
Gateway
Almost Periodic
Optimization Techniques
Telecommunication networks
Degradation
Update
Partition
Model
Evaluate
Experiment
Communication

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Matsumoto, K., & Hashimoto, K. (1999). Intelligent monitoring method using time varying binomial distribution models for pseudo-periodic communication traffic. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1642, pp. 123-134). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1642). Springer Verlag. https://doi.org/10.1007/3-540-48412-4_11

Intelligent monitoring method using time varying binomial distribution models for pseudo-periodic communication traffic. / Matsumoto, Kazunori; Hashimoto, Kazuo.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1642 Springer Verlag, 1999. p. 123-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1642).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Matsumoto, K & Hashimoto, K 1999, Intelligent monitoring method using time varying binomial distribution models for pseudo-periodic communication traffic. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1642, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1642, Springer Verlag, pp. 123-134, 3rd International Symposium on Intelligent Data Analysis, IDA 1999, Amsterdam, Netherlands, 99/8/9. https://doi.org/10.1007/3-540-48412-4_11
Matsumoto K, Hashimoto K. Intelligent monitoring method using time varying binomial distribution models for pseudo-periodic communication traffic. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1642. Springer Verlag. 1999. p. 123-134. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-48412-4_11
Matsumoto, Kazunori ; Hashimoto, Kazuo. / Intelligent monitoring method using time varying binomial distribution models for pseudo-periodic communication traffic. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1642 Springer Verlag, 1999. pp. 123-134 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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