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A comparison of learning methods over raw data: forecasting cab services market share in New York City

  • Fernando Turrado García
  • Luis Javier García Villalba
  • Ana Lucila Sandoval Orozco
  • Tai-Hoon Kim
Article
  • 16 Downloads

Abstract

The cab services, present in most of the cities, are one of the most used offerings for passenger transportation. Nowadays their business model is being threatened by the meddling of emerging third parties powered by modern technologies. Based on the New York cab data, we will make a comparison of several machine learning techniques (linear regression, support vector machines and random forest) for forecasting the amount of dollars spent in the cab service. The comparison of those methods will focus on the accuracy of their forecasts under several circumstances: real data applied to all features, some noisy data (real data with some uniform distributed noise added) applied to several key features and some estimated data (obtained from other statistical estimators) applied to the key features. The main goal of this comparison is to provide some data regarding the performance of those methods when they are used in conjunction with other estimators

Keywords

Forecast Linear regression Random forest Support vector machines Time series 

Notes

Acknowledgements

This research work was supported by Sungshin Women’s University. In addition, L.J.G.V. and A.L.S.O thanks the European Commission Horizon 2020 5G-PPP Programme (Grant Agreement number H2020-ICT-2014-2/671672-SELFNET - Framework for Self-Organized Network Management in Virtualized and Software-Defined Networks).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Fernando Turrado García
    • 1
  • Luis Javier García Villalba
    • 1
  • Ana Lucila Sandoval Orozco
    • 1
  • Tai-Hoon Kim
    • 2
  1. 1.Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Information Technology and Computer Science, Office 431Universidad Complutense de Madrid (UCM)MadridSpain
  2. 2.Department of Convergence SecuritySungshin Women’s UniversitySeoulKorea

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