Abstract
Telecommunication companies face the challenge to reduce the number of service request openings (SROs). A predictive behavior able to reduce this number can improve customers experience and decrease operational costs. This paper proposes a machine learning (ML) based approach to reduce the number of SROs. For such, it uses real data from a Brazilian telecom operator. The proposed approach uses feature-based time series extracted from network equipment’s signals, modeling the problem as a binary classification task. We carry out experiments to investigate the impact of long-term and short-term windows in the predictive performance. After pre-processing the data, we apply different classifiers algorithms. According to experimental results, a high predictive performance was obtained, mainly when long-term network behavior data was used. These results have a positive impact in the company costs.
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
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Int. Res. 16(1), 321–357 (2002)
Crawshaw, J.: AI in telecom operations: Opportunities & obstacles. Technical report, Heavy Reading Reports (2018)
Diaz-Aviles, E., et al.: Towards real-time customer experience prediction for telecommunication operators. In: IEEE International Conference on Big Data, pp. 1063–1072 (2015)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_1
Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2000)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)
García, D.L., Nebot, À., Vellido, A.: Intelligent data analysis approaches to churn as a business problem: a survey. Knowl. Inf. Syst. 51(3), 719–774 (2017)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Huang, H., et al.: A cross-platform consumer behavior analysis of large-scale mobile shopping data. In: Proceedings of the 2018 World Wide Web Conference (WWW 2018), Geneva, Switzerland, pp. 1785–1794 (2018)
Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, New York (2011)
Jin, S., Zhang, Z., Chakrabarty, K., Gu, X.: Failure prediction based on anomaly detection for complex core routers. In: Proceedings of the International Conference on Computer-Aided Design, ICCAD 2018, pp. 49:1–49:6 (2018)
Kleist, C.: Time series data mining methods: a review. Master’s thesis, Humboldt-Universitat zu Berlin (2015)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms (2014)
Ouyang, Y., Hu, M.: Big Data Applications in the Telecommunications Industry. IGI Global, Pennsylvania (2016)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Prashanth, R., Deepak, K., Meher, A.K.: High accuracy predictive modelling for customer churn prediction in telecom industry. In: Perner, P. (ed.) MLDM 2017. LNCS (LNAI), vol. 10358, pp. 391–402. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62416-7_28
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386 (1958)
Susto, G.A., Cenedese, A., Terzi, M.: Chapter 9 - time-series classification methods: review and applications to power systems data. In: Arghandeh, R., Zhou, Y. (eds.) Big Data Application in Power Systems, pp. 179–220. Elsevier (2018)
Totok, A.: Modern Internet Services: Exploiting Service Usage Information for Optimizing Service Management. VDM Verlag (2009)
Wei, C.P., Chiu, I.T.: Turning telecommunications call details to churn prediction: a data mining approach. Expert Syst. Appl. 23(2), 103–112 (2002)
Zhang, J., Fu, J., Zhang, C., Ke, X., Hu, Z.: Not too late to identify potential churners: early churn prediction in telecommunication industry. In: IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 194–199. BDCAT (2016)
Acknowledgements
We thank Algar Telecom/Brain for financial, technical and legal support; Federal University of Uberlândia (UFU) and University of São Paulo (USP) for research, legal and administrative support; and Fundação de Apoio Universitário (FAU/UFU) for financial management.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pereira, F.S.F. et al. (2019). Feature-Based Time Series Classification for Service Request Opening Prediction in the Telecom Industry. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-30244-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30243-6
Online ISBN: 978-3-030-30244-3
eBook Packages: Computer ScienceComputer Science (R0)