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Forecasting China Future MNP by Deep Learning

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

Abstract

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.

Keywords

Mobile number portability Deep learning Multi-variable forecasting 

Notes

Acknowledgement

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.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Shimin Hu
    • 1
  • 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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