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Online Data Flow Prediction Using Generalized Inverse Based Extreme Learning Machine

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Advanced Multimedia and Ubiquitous Engineering (MUE 2018, FutureTech 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 518))

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Abstract

Accurate prediction of data flow has been a major problem in big data scenarios. Traditional predictive models require expert knowledge and long training time, which leads to a time-consuming update of the models and further hampers the use in real-time processing scenarios. To relief the problem, we combined Extreme Learning Machine with sliding window technique to track data flow trends, in which rank-one updates of generalized inverse is used to further calculate a stable parameter for the model. Experimental on real traffic flow data collected along US60 in Phoenix freeway in 2011 were conducted to evaluate the proposed method. The results confirm that the proposed model has more accurate average prediction performance compared with other methods in all 12 months.

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Author information

Authors and Affiliations

  1. China Electronics Technology Group Corporation No. 10 Research Institute, Chengdu, China

    Ying Jia

Authors
  1. Ying Jia
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Corresponding author

Correspondence to Ying Jia .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    James J. Park

  2. Departement of Business Science, University of Salerno, Fisciano, Italy

    Vincenzo Loia

  3. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  4. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

    Gangman Yi

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Jia, Y. (2019). Online Data Flow Prediction Using Generalized Inverse Based Extreme Learning Machine. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_25

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  • DOI: https://doi.org/10.1007/978-981-13-1328-8_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1327-1

  • Online ISBN: 978-981-13-1328-8

  • eBook Packages: EngineeringEngineering (R0)

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