Enterprise System Response Time Prediction Using Non-stationary Function Approximations

  • K. Ravikumar
  • Kriti KumarEmail author
  • Naveen ThokalaEmail author
  • M. Girish ChandraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11506)


We consider the problem of predicting response time of a large scale enterprise system using causal forecasting models. Specifically, the problem pertains to predicting potential system failure well in advance so that preventive actions can be initiated. Various influential factors are identified and their relationship with the system response time is estimated from data using non-stationary (time dependent) functional approximations. Experimental results on the prediction performance of different methods are presented and their discriminative characteristics with regard to error distribution are used to suggest a recommendation for practical implementation.


Multivariate time series forecasting Machine learning Enterprise systems Predictive analytics 


  1. 1.
    Montgomery, C.D., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting. Wiley, Hoboken (2008)zbMATHGoogle Scholar
  2. 2.
    Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976)zbMATHGoogle Scholar
  3. 3.
    Moneta, A., Spirtes, P.: Graphical models for the identification of causal structures in multivariate time series models. In: Proceeding of Fifth International Conference on Computational Intelligence in Economics and Finance (2006)Google Scholar
  4. 4.
    Dahlhaus, R.: Graphical interaction models for multivariate time series. Metrika 51(2), 157–172 (2000)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Spiegel, S., Gaebler, J., Lommatzsch, A., Luca, E., Albayrak, S.: Pattern recognition and classification for multivariate time series. In: SensorKDD 2011, San Diego (2011)Google Scholar
  6. 6.
    Cheng, H., Tan, P., Gao, J., Scripps, J.: Multistep-ahead time series prediction. In: PAKDD, pp. 765–774 (2006)Google Scholar
  7. 7.
    Kline, D.M.: Methods for multi-step time series forecasting with neural networks. In: Peter Zhang, G. (ed.) Neural Networks in Business Forecasting, pp. 226–250. Information Science Publishing, Hershey (2004)CrossRefGoogle Scholar
  8. 8.
    Smola, A., Scholkopf, B.: A tutorial on support vector regression. J. Stat. Comput. 14(3), 199–222 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.BangaloreIndia
  2. 2.TCS Research and InnovationBangaloreIndia

Personalised recommendations