Abstract
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.
K. Ravikumar was with TCS R&I till recently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Brockwell, P.J., Davis, R.A., Calder, M.V.: Introduction to Time Series and Forecasting, vol. 2. Springer, New York (2002)
Schmidt, M.-T., et al.: The enterprise service bus: making service-oriented architecture real. IBM Syst. J. 44(4), 781–797 (2005)
Freeman, J.R.: Granger causality and the times series analysis of political relationships. Am. J. Polit. Sci. 27(2), 327–358 (1983)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)
Hill, T., O’Connor, M., Remus, W.: Neural network models for time series forecasts. Manage. Sci. 42(7), 1082–1092 (1996)
Ojemakinde, B.T.: Support vector regression for non-stationary time series (2006)
Tealab, A., Hefny, H., Badr, A.: Forecasting of nonlinear time series using ANN. Future Comput. Inform. J. 2(1), 39–47 (2017)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
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). https://doi.org/10.1007/978-3-319-11310-4_71
Tyralis, H., Papacharalampous, G.: Variable selection in time series forecasting using random forests. Algorithms 10(4), 114 (2017)
Verikas, A., Gelzinis, A., Bacauskiene, M.: Mining data with random forests: a survey and results of new tests. Pattern Recogn. 44, 330–349 (2011)
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)
Ahmed, N.K., et al.: An empirical comparison of machine learning models for time series forecasting. Econom. Rev. 29(5–6), 594–621 (2010)
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
Thokala, N.K., Kumar, K., Girish Chandra, M., Ravikumar, K. (2019). Long-Term Forecasting of Heterogenous Variables with Automatic Algorithm Selection. 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_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-20521-8_16
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)