Abstract
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.
K. Ravikumar was with TCS Research and Innovation till recently.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Montgomery, C.D., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting. Wiley, Hoboken (2008)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976)
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)
Dahlhaus, R.: Graphical interaction models for multivariate time series. Metrika 51(2), 157–172 (2000)
Spiegel, S., Gaebler, J., Lommatzsch, A., Luca, E., Albayrak, S.: Pattern recognition and classification for multivariate time series. In: SensorKDD 2011, San Diego (2011)
Cheng, H., Tan, P., Gao, J., Scripps, J.: Multistep-ahead time series prediction. In: PAKDD, pp. 765–774 (2006)
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)
Smola, A., Scholkopf, B.: A tutorial on support vector regression. J. Stat. Comput. 14(3), 199–222 (2004)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ravikumar, K., Kumar, K., Thokala, N., Chandra, M.G. (2019). Enterprise System Response Time Prediction Using Non-stationary Function Approximations. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-20521-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20520-1
Online ISBN: 978-3-030-20521-8
eBook Packages: Computer ScienceComputer Science (R0)