Long-Term Forecasting of Heterogenous Variables with Automatic Algorithm Selection

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


An Enterprise System Bus (ESB) is a software which is used to communicate between various mutually interacting software applications in manufacturing plants. ESB performance is very important for the smooth functioning of the system. Any degradation or failure of the ESB results in huge revenue loss due to production discontinuity. Therefore, maintaining ESB in a healthy state is very essential and there are multiple factors related to resource utilization, workload and number of interfaces etc., which influences the performance of the ESB. Forecasting these variables at least a day ahead (24 h ahead) is required to take appropriate actions by the business team to maintain the ESB performance under control. But, these variables are heterogeneous (continuous, discrete and percentages) in nature, highly non-linear and non-stationary. The challenges associated with forecasting of these variables are (i) long horizon (24 h ahead forecast at 5 min granularity requires to forecast 288 steps) (ii) data generated from these kinds of systems makes it very difficult to use any linear statistical methods like state-space models, ARIMA etc. To address these challenges, the paper presents a framework where a basket of learning algorithms based on Artificial Neural Network (ANN), Support Vector Regression (SVR) and Random Forests (RF) were used to model the chaotic behavior of the time series with a real-time automatic algorithm selection mechanism which enables appropriate forecasting algorithm to be chosen dynamically based on the performance over a time window, resulting in different algorithms being used for forecasting the same target variable on different days. Importance of the proposed strategy was demonstrated with suitable forecasting results for different variables/parameters impacting the performance of the critical Enterprise System Bus of an automotive manufacturing setup.


  1. 1.
    Bhadoria, R.S., Chaudhari, N.S., Tomar, G.S.: The performance metric for enterprise system bus (ESB) in SOA system: theoretical underpinnings and empirical illustrations for information processing. Inf. Syst. 65, 158–171 (2017)CrossRefGoogle Scholar
  2. 2.
    Brockwell, P.J., Davis, R.A., Calder, M.V.: Introduction to Time Series and Forecasting, vol. 2. Springer, New York (2002)CrossRefGoogle Scholar
  3. 3.
    Schmidt, M.-T., et al.: The enterprise service bus: making service-oriented architecture real. IBM Syst. J. 44(4), 781–797 (2005)CrossRefGoogle Scholar
  4. 4.
    Freeman, J.R.: Granger causality and the times series analysis of political relationships. Am. J. Polit. Sci. 27(2), 327–358 (1983)CrossRefGoogle Scholar
  5. 5.
    Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hill, T., O’Connor, M., Remus, W.: Neural network models for time series forecasts. Manage. Sci. 42(7), 1082–1092 (1996)CrossRefGoogle Scholar
  7. 7.
    Ojemakinde, B.T.: Support vector regression for non-stationary time series (2006)Google Scholar
  8. 8.
    Tealab, A., Hefny, H., Badr, A.: Forecasting of nonlinear time series using ANN. Future Comput. Inform. J. 2(1), 39–47 (2017)CrossRefGoogle Scholar
  9. 9.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  10. 10.
    Dudek, G.: Short-term load forecasting using random forests. In: Filev, D., et al. (eds.) Intelligent Systems’2014. AISC, vol. 323, pp. 821–828. Springer, Cham (2015). Scholar
  11. 11.
    Tyralis, H., Papacharalampous, G.: Variable selection in time series forecasting using random forests. Algorithms 10(4), 114 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Verikas, A., Gelzinis, A., Bacauskiene, M.: Mining data with random forests: a survey and results of new tests. Pattern Recogn. 44, 330–349 (2011)CrossRefGoogle Scholar
  13. 13.
    Marcellino, M., Stock, J.H., Watson, M.W.: A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J. Econom. 135(1–2), 499–526 (2006)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ahmed, N.K., et al.: An empirical comparison of machine learning models for time series forecasting. Econom. Rev. 29(5–6), 594–621 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

Personalised recommendations