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

Kyalo Richard; Waititu Anthony; Wanjoya Anthony

doi:doi:10.11648/j.ajtas.20140303.11

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

Kyalo Richard, Waititu Anthony, Wanjoya Anthony

Published in American Journal of Theoretical and Applied Statistics (Volume 3, Issue 3)

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.
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[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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[11]	Pacelli1, V., Bevilaqua, V., Azzollini, M. (2011), An Artificial Neural Network Model to Forecast Exchanges rates, Journal for Intelligent Learning Systems and Applications, 3:57 - 69.
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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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TY  - JOUR
T1  - Artificial Neural Network Application in Modelling Revenue Returns from Mobile Payment Services in Kenya
AU  - Kyalo Richard
AU  - Waititu Anthony
AU  - Wanjoya Anthony
Y1  - 2014/05/10
PY  - 2014
N1  - https://doi.org/10.11648/j.ajtas.20140303.11
DO  - 10.11648/j.ajtas.20140303.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 60
EP  - 64
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20140303.11
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.
VL  - 3
IS  - 3
ER  -

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Author Information

Kyalo Richard

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

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

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

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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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TY  - JOUR
T1  - Artificial Neural Network Application in Modelling Revenue Returns from Mobile Payment Services in Kenya
AU  - Kyalo Richard
AU  - Waititu Anthony
AU  - Wanjoya Anthony
Y1  - 2014/05/10
PY  - 2014
N1  - https://doi.org/10.11648/j.ajtas.20140303.11
DO  - 10.11648/j.ajtas.20140303.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 60
EP  - 64
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20140303.11
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.
VL  - 3
IS  - 3
ER  -

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