Survival Model for WiFi Usage Forecasting in National Formosa University

  • Jutarat Kositnitikul
  • Ji-Han JiangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)


This paper presents the effectiveness of adopting survival analysis approach to predict the WiFi usage in the future and the understanding of covariance affect WiFi usage such as date, time, and user, by introducing dataset of WiFi usage historical. The study took place in National Formosa University in Taiwan. Survival analysis is the analysis of data involving times to event of interest. There are three survival analysis methods implemented in this paper which are Kaplan-Meier estimator, Cox Proportional Hazards Model, and Random Survival Forest. The result was shown that survival analysis approach gains a satisfy prediction result. This approach can be adapted for improving WiFi network organization in any organization by understanding the connection of covariance and accomplishing an effective decision.


WiFi prediction Survival analysis Kaplan Meier survival Cox Proportional Hazards Random Survival Forest 


  1. 1.
    Pan, D.: Analysis of Wi-Fi performance data for a Wi-Fi throughput prediction approach. KTH (2017)Google Scholar
  2. 2.
    iPass Corporate: iPass Mobile Professional Report 2016. iPass company (2016)Google Scholar
  3. 3.
    Kartsonaki, C.: Survival Analysis. University of Oxford, Oxford (2016)zbMATHGoogle Scholar
  4. 4.
    Mills, M.: Introducing Survival and Event History Analysis. SAGE Publications, London (2011)CrossRefGoogle Scholar
  5. 5.
    Kleinbaum, D.G.: Survival Analysis, a Self Learning Text. Springer, New York (1996)CrossRefGoogle Scholar
  6. 6.
    Smith, T., Smith, B.: Kaplan Meier and Cox proportional hazards modeling: hands on survival analysis. SAS® Users Group International Proc. Seattle, Washington (2003)Google Scholar
  7. 7.
    Louzada, F., Cancho, V.G., Oliveira, M.R., Yiqi, B.: Modeling time to default on a personal loan portfolio in presence of disproportionate hazard rates. J. Stat. Appl. Pro. 3(3), 295–305 (2014)Google Scholar
  8. 8.
    Ishwaran, H., Kogalur, U.B., Blackstone, E.H., Lauer, M.S.: Random survival forests. Cleveland Clinic, Columbia University (2018)Google Scholar
  9. 9.
    Mogensen, U.B., Ishwaran, H., Gerds, A.: Evaluating random forests for survival analysis using prediction error curves. Department of Biostatistics, University of Copenhagen (2012)Google Scholar
  10. 10.
    R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2014)Google Scholar
  11. 11.
    Therneau, T.M., Lumley, T.: (core) (2009)
  12. 12.
    Ishwaran, H., Kogalur, U.B.: (2014)
  13. 13.
    Ehrlinger, J.: ggRandomForests: Exploring Random Forest Survival. Microsoft (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Formosa UniversityHuweiTaiwan

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