Forecasting China Future MNP by Deep Learning

  • Shimin HuEmail author
  • Mengyu Liu
  • Simon Fong
  • Wei Song
  • Nilanjan Dey
  • Raymond Wong
Part of the International Series on Computer Entertainment and Media Technology book series (ISCEMT)


The objective of this study is to find a most accurate way to forecast the future of the Mobile Number Portability (MNP) users in Mainland China. We propose a simplified MNP AD System that suits Chinese situation. MNP is an optional value-added-service through which customers can retain their assigned mobile telephone numbers but change their subscriptions from one mobile network operator to another. The service has been on the move for more than 19 years over 80 countries in the world except Mainland China, even with Macau and Hong Kong. Consequently, relatively few data from China are available, and the insufficiency of training data poses a forecasting challenge. Sixteen machine learning methods including contemporary deep learning algorithms are used in an attempt of forecasting the future MNP of China; however, the prediction accuracy is acceptable only for large dataset. When the dataset is small, univariable time series forecasting fail to predict with reliability. By introducing more factors that are related to the forecasting objective to the dataset (turning it multi-variable), the accuracy improves with error rate drops. The accuracy is found to further rise after removing some irrelevant factors. Finally propose some recommendations, simplified process, and a centralized MNP AD system with less human work that is applicable to Mainland China are proposed. The system is easy for government to control the porting and better forecast as business intelligence use.


Mobile number portability Deep learning Multi-variable forecasting 



The authors are thankful for the financial support from the Research Grants (1) title: “Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF)”, Grant no. MYRG2015-00128-FST, offered by the University of Macau, and Macau SAR government. (2) title: “A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel”, Grant no. FDCT/126/2014/A3, offered by FDCT of Macau SAR government.


  1. 1.
    Gurjeet Kaur, Ritika Sambyal, Exploring Predictive Switching Factors for Mobile Number Portability, Vikalpa: The Journal for Decision Makers, SAGE, Volume: 41 issue: 1, page(s): 74–95Google Scholar
  2. 2.
    BelénUsero Sánchez, Grigorios Asimakopoulos, Regulation and competition in the European mobile communications industry: An examination of the implementation of mobile number portability, Telecommunications Policy, Elsevier, Volume 36, Issue 3, April 2012, Pages 187–196CrossRefGoogle Scholar
  3. 3.
    Miao Miao, Jia Jia, Rui Tingting, Xiong Fangping and Li haibo, Research on Influencing Factors of Chinese Mobile Communication Customers’ Switching Intention by Mobile Number Portability, International Journal of Future Generation Communication and Networking, Vol. 9, No. 4 (2016), pp. 229–238CrossRefGoogle Scholar
  4. 4.
    Stefan Buehler, Justus Haucap, Mobile Number Portability, Journal of Industry, Competition and Trade, September 2004, Volume 4, Issue 3, pp 223–238CrossRefGoogle Scholar
  5. 5.
    Peter J. Brockwell, Richard A. Davis, Introduction to Time Series and Forecasting, Third edition, Springer, 2016Google Scholar
  6. 6.
    Sepp Hochreiter; Jürgen Schmidhuber (1997). “Long short-term memory”. Neural Computation. 9 (8): 1735–1780CrossRefGoogle Scholar
  7. 7.
    Antonio Gulli, Deep Learning with Keras, Packt Publishing, 26 Apr 2017Google Scholar
  8. 8.
    8Sven Mayer, Huy Viet Le, Niels Henze, Machine learning for intelligent mobile user interfaces using TensorFlow, Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services, Article No. 62, Vienna, Austria — September 04–07, 2017Google Scholar
  9. 9.
    Cowpertwait, Paul S.P., Metcalfe, Andrew V., Introductory Time Series with R, Springer-Verlag, 2009Google Scholar
  10. 10.
    Dimitris N. Politis, Model-Based Prediction in Autoregression, Springer 2015Google Scholar
  11. 11.
    Andreas C. Damianou, Neil D. Lawrence, Deep Gaussian Processes, Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) 2013, Scottsdale, AZ, USA. Volume 31 of JMLRGoogle Scholar
  12. 12.
    David J.C. Mackay, Introduction to Gaussian Processes, May 1998Google Scholar
  13. 13.
    MacKay, David, J.C. (2003). Information Theory, Inference, and Learning Algorithms (PDF). Cambridge University Press. p. 540. ISBN 9780521642989Google Scholar
  14. 14.
    Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961Google Scholar
  15. 15.
    Chang, Chih-Chung; Lin, Chih-Jen (2011). “LIBSVM: A library for support vector machines”. ACM Transactions on Intelligent Systems and Technology. 2 (3)CrossRefGoogle Scholar
  16. 16.
    Betul Bostanci and Erkan Bostanci, An Evaluation of Classification Algorithms Using Mc Nemar’s Test, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), Advances in Intelligent Systems, pp.15–26Google Scholar
  17. 17.
    Altman, N. S. (1992). “An introduction to kernel and nearest-neighbor nonparametric regression”. The American Statistician. 46 (3): 175–185Google Scholar
  18. 18.
    P.K. Douglas, Sam Harris, Alan Yuille, Mark S. Cohena, Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief, Neuroimage. 2011 May 15; 56(2): 544–553CrossRefGoogle Scholar
  19. 19.
    Yingying Fan, Gareth M. James, Peter Radchenko, Functional additive regression, Annals of Statistics, 2015, Vol. 43, No. 5, 2296–2325CrossRefGoogle Scholar
  20. 20.
    Mary Ellen Foster, Andre Gaschler, Manuel Giuliani, Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction, International Journal of Social Robotics, Springer, pp 1–16Google Scholar
  21. 21.
    Masahiro Inuiguchi, Ryuta Enomoto, Yoshifumi Kusunoki, Non-hierarchical Clustering of Decision Tables toward Rough Set-Based Group Decision Aid, International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2010: Modeling Decisions for Artificial Intelligence pp 195–206Google Scholar
  22. 22.
    Haelterman, Rob (2009). “Analytical study of the least squares quasi-Newton method for interaction problems”. PhD Thesis, Ghent University. Retrieved 2014–08-14Google Scholar
  23. 23.
    Wang K, Song W, Li J, Lu W, Yu J, Han X., The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China, Asia Pac J Public Health. 2016 May;28(4):336–46CrossRefGoogle Scholar
  24. 24.
    Dariusz Grzesica, The Decomposition Issue of a Time Series in the Forecasting Process, International conference Knowledge-based Organization,, Volume 23, Issue 3, (Jun 2017)Google Scholar
  25. 25.
    Broyden, C. G. (1970), “The convergence of a class of double-rank minimization algorithms”, Journal of the Institute of Mathematics and Its Applications, 6: 76–90CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Shimin Hu
    • 1
    Email author
  • Mengyu Liu
    • 1
  • Simon Fong
    • 1
  • Wei Song
    • 2
  • Nilanjan Dey
    • 3
  • Raymond Wong
    • 4
  1. 1.Department of Computer and Information ScienceUniversity of MacauTaipaChina
  2. 2.School of Computer Science and Technology, North China University of TechnologyBeijingChina
  3. 3.Department of ITTechno India College of TechnologyKolkataIndia
  4. 4.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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