Traffic data analysis based on extreme value theory and its applications to predicting unknown serious deterioration

Masato Uchida*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


It is important to predict serious deterioration of telecommunication quality. This paper investigates predicting such serious events by analyzing only a "short" period (i.e., a "small" amount) of teletraffic data. To achieve this end, this paper presents a method for analyzing the tail distributions of teletraffic state variables, because tail distributions are suitable for representing serious events. This method is based on Extreme Value Theory (EVT), which provides a firm theoretical foundation for the analysis. To be more precise, in this paper, we use throughput data measured on an actual network during daily busy hours for IS minutes, and use its first 10 seconds (known data) to analyze the tail distribution. Then, we evaluate how well the obtained tail distribution can predict the tail distribution of the remaining 890 seconds (unknown data). The results indicate that the obtained tail distribution based on EVT by analyzing the small amount of known data can predict the tail distribution of unknown data much better than methods based on empirical or log-normal distributions. Furthermore, we apply the obtained tail distribution to predict the peak throughput in unknown data. The results of this paper enable us to predict serious deterioration events with lower measurement cost.

Original languageEnglish
Pages (from-to)2654-2664
Number of pages11
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - 2004 Dec
Externally publishedYes


  • Extreme value theory
  • Prediction
  • Serious deterioration of the telecommunication quality
  • Tail distribution

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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