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Artificial Neural Network Application in Modelling Revenue Returns from Mobile Payment Services in Kenya

Received: 28 March 2014     Accepted: 25 April 2014     Published: 10 May 2014
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Abstract

Artificial Neural Networks has recently shown a great applicability in time-series analysis and forecasting thus correctly deducing the unseen part of the population even if the sample data contain noisy information. In this paper we used Neural Network to model revenue returns from mobile payment services using dataset extracted from Central Bank of Kenya website. The network with one or two hidden layers was tested with various combination of neurons, and results were compared in terms of forecasting error. It was observed that ANN if properly trained accurately forecast Revenue returns on mobile payments services in Kenya.

Published in American Journal of Theoretical and Applied Statistics (Volume 3, Issue 3)
DOI 10.11648/j.ajtas.20140303.11
Page(s) 60-64
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Neural Network, Quasi Newton, Forecasting, Generalization

References
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[2] Mwita, P., Franke, J., Odhiambo, R. and Waititu, A. (2005). On conditional quantiles: Direct Kernel Estimator and its Consistency. African Journal of Science and Technology, Vol. 6(2), 67-76.
[3] Tang , Almeida and Fishwick, Simula-tion, Time series forecasting using neural networks vs. Box-Jenkins methodology ,November 1991, pp303- 310.
[4] Medeiros M, Terasvirta, T, Rech, G. (2006) “Building Neural Network Models for Time Series: A Statistical Approach.” Journal of Forecasting. 25(1) pp. 49-75.
[5] Zhang, G., Patuwo, B.E., Hu, M.Y. (1998), Forecasting with artificial neural networks: The state of the art International journal of forecasting, 14:35 -62.
[6] Mahdavi Gh, Behmanesh MR (2005). The Forecasting of Stock Price of Investment Firms by Using Artificial Neural Networks. J. Econom. Res. 19(4): 211-233.
[7] Zhong Luo, Liu Li-sheng. The application of Neural Network in Lifetime Prediction of Concrete. Journal of Wuhan University of Technology. 2002,17:79-81.
[8] Teräsvirta T, Lin, C-FJ. 1993. Determining the number of hidden units in a single hidden-layer neural network model. Research Report 1993/7, Bank of Norway.
[9] J. Yao, Y. Li and C. L. Tan, “Option price forecasting using neural networks,” OMEGA: Int. Journal of Management Science, vol. 28, pp 455-466, 2000.
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Cite This Article
  • APA Style

    Kyalo Richard, Waititu Anthony, Wanjoya Anthony. (2014). Artificial Neural Network Application in Modelling Revenue Returns from Mobile Payment Services in Kenya. American Journal of Theoretical and Applied Statistics, 3(3), 60-64. https://doi.org/10.11648/j.ajtas.20140303.11

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    ACS Style

    Kyalo Richard; Waititu Anthony; Wanjoya Anthony. Artificial Neural Network Application in Modelling Revenue Returns from Mobile Payment Services in Kenya. Am. J. Theor. Appl. Stat. 2014, 3(3), 60-64. doi: 10.11648/j.ajtas.20140303.11

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    AMA Style

    Kyalo Richard, Waititu Anthony, Wanjoya Anthony. Artificial Neural Network Application in Modelling Revenue Returns from Mobile Payment Services in Kenya. Am J Theor Appl Stat. 2014;3(3):60-64. doi: 10.11648/j.ajtas.20140303.11

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  • @article{10.11648/j.ajtas.20140303.11,
      author = {Kyalo Richard and Waititu Anthony and Wanjoya Anthony},
      title = {Artificial Neural Network Application in Modelling Revenue Returns from Mobile Payment Services in Kenya},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {3},
      number = {3},
      pages = {60-64},
      doi = {10.11648/j.ajtas.20140303.11},
      url = {https://doi.org/10.11648/j.ajtas.20140303.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20140303.11},
      abstract = {Artificial Neural Networks has recently shown a great applicability in time-series analysis and forecasting thus correctly deducing the unseen part of the population even if the sample data contain noisy information. In this paper we used Neural Network to model revenue returns from mobile payment services using dataset extracted from Central Bank of Kenya website. The network with one or two hidden layers was tested with various combination of neurons, and results were compared in terms of forecasting error. It was observed that ANN if properly trained accurately forecast Revenue returns on mobile payments services in Kenya.},
     year = {2014}
    }
    

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    AU  - Kyalo Richard
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    AU  - Wanjoya Anthony
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    AB  - Artificial Neural Networks has recently shown a great applicability in time-series analysis and forecasting thus correctly deducing the unseen part of the population even if the sample data contain noisy information. In this paper we used Neural Network to model revenue returns from mobile payment services using dataset extracted from Central Bank of Kenya website. The network with one or two hidden layers was tested with various combination of neurons, and results were compared in terms of forecasting error. It was observed that ANN if properly trained accurately forecast Revenue returns on mobile payments services in Kenya.
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Author Information
  • Jomo Kenyatta University Department of Statistics and Actuarial Science, Nairobi, Kenya

  • Jomo Kenyatta University Department of Statistics and Actuarial Science, Nairobi, Kenya

  • Jomo Kenyatta University Department of Statistics and Actuarial Science, Nairobi, Kenya

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