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Modeling Export Price of Tea in Kenya: Comparison of Artificial Neural Network and Seasonal Autoregressive Integrated Moving Average

Received: 13 November 2014     Accepted: 21 November 2014     Published: 19 December 2014
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Abstract

Agriculture sector is a key driver of economic growth in Kenya. It remains the main source of livelihood for the majority of the Kenyan people. Tea, coffee, and horticulture are the main agricultural exports in Kenya. Export price of these commodities fluctuates mainly due to law of demand and supply. Other reasons include; quality of goods and inflation effect on the dollar or other hard currencies. Further, farmers and their cooperative societies are affected by the local foreign exchange. The government and other stake holders require prior information on price trends for ease of planning. Thus it is important to forecast export prices of these commodities. The purpose of this study is to compare the forecasting performance of artificial neural network (ANN) model and a SARIMA model in export price of tea in Kenya. Secondary data was obtained from Kenya National Bureau of Statistics (KNBS). A total of 185 monthly export prices were obtained. A three layer feed-forward artificial neural network was trained using 70% of the data. The ANN model obtained was used to predict export prices for the remaining 30% of the data. SARIMA model was also used to predict export prices for the same duration. Forecasting performance was evaluated using Root mean squared errors (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). ANN demonstrated a superior performance over SARIMA model. The authors' ANN has high performance compared to SARIMA and can accurately predict export price of tea.

Published in American Journal of Theoretical and Applied Statistics (Volume 3, Issue 6)
DOI 10.11648/j.ajtas.20140306.16
Page(s) 211-216
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

Artificial Neural Network (ANN), Seasonal Autoregressive Integrated Moving Average (SARIMA), Kenya National Bureau of Statistics (KNBS)

References
[1] Box, G. P. and Jenkins, G. M. (1976), “Time Series Analysis, Forecasting and Control”. Holden-Day. San Francisco. CA.
[2] Tanaka, H. (1987): “Fuzzy Data Analysis by Possibility Linear Models”. Fuzzy Sets and Systems. 24( 3), 363-375
[3] Hornik, K., M. Stinchcombe, and H. White (1989): “Multi-Layer Feed forward Networks are Universal Approximators,” Neural Networks, 2, 359—366
[4] Refenes A. N., A. Zapranis, and G. Francis,(1994) “Stock Performance Modeling Using Neural Networks: A Comparative Study with Regression Models”, Elsevier B.V, 5, 961-970.
[5] Hutchinson J., W. Andrew, & T. Poggio (1994). “A non-parametric approach to pricing and hedging derivative securities via learning networks”. Journal of Finance, 49, 851-889.
[6] Zhang, G., and Hu, M.Y. (1998), Neural network forecasting of the British pound/US dollar exchange rate. International Journal of Management Science, 26, 495 – 506.
[7] Monica Adya and Fred Collopy,(1998), “How effective are neural networks at forecasting and predicting? A review and evaluation”, Journal of Forecasting, 17, 481-495.
[8] Shekhar, S. (2004). “Recursive Methods for Forecasting Short-Term Traffic Flow using Seasonal ARIMA Time Series model”. Master’s thesis. Graduate Faculty of North Carolina State University. Raleigh. North Carolina. USA.
[9] Kyungjoo Lee, Sehwan Yoo, John Jongdae Jin,(2007) “Neural network model vs. SARIMA model in forecasting Korean stock price index”, Issues in Information Systems, 8,(2),372-378.
[10] Cao Q, Parry M (2009), Neural Network Earnings per Share Forecasting Models: A Comparison of Backward Propagation and the Genetic Algorithm. Decision Support System. International journal, 47, 32-41.
[11] Zhang, B. Eddy Patuwo, Michael Y. Hu ,(1998) “Forecasting with artificial neural networks: The state of the art”, International journal of forecasting, 14 ,35–62.
[12] 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, 60-64.
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  • APA Style

    Mbiriri Ikonya, Peter Mwita, Anthony Wanjoya. (2014). Modeling Export Price of Tea in Kenya: Comparison of Artificial Neural Network and Seasonal Autoregressive Integrated Moving Average. American Journal of Theoretical and Applied Statistics, 3(6), 211-216. https://doi.org/10.11648/j.ajtas.20140306.16

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    ACS Style

    Mbiriri Ikonya; Peter Mwita; Anthony Wanjoya. Modeling Export Price of Tea in Kenya: Comparison of Artificial Neural Network and Seasonal Autoregressive Integrated Moving Average. Am. J. Theor. Appl. Stat. 2014, 3(6), 211-216. doi: 10.11648/j.ajtas.20140306.16

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    AMA Style

    Mbiriri Ikonya, Peter Mwita, Anthony Wanjoya. Modeling Export Price of Tea in Kenya: Comparison of Artificial Neural Network and Seasonal Autoregressive Integrated Moving Average. Am J Theor Appl Stat. 2014;3(6):211-216. doi: 10.11648/j.ajtas.20140306.16

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  • @article{10.11648/j.ajtas.20140306.16,
      author = {Mbiriri Ikonya and Peter Mwita and Anthony Wanjoya},
      title = {Modeling Export Price of Tea in Kenya: Comparison of Artificial Neural Network and Seasonal Autoregressive Integrated Moving Average},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {3},
      number = {6},
      pages = {211-216},
      doi = {10.11648/j.ajtas.20140306.16},
      url = {https://doi.org/10.11648/j.ajtas.20140306.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20140306.16},
      abstract = {Agriculture sector is a key driver of economic growth in Kenya. It remains the main source of livelihood for the majority of the Kenyan people. Tea, coffee, and horticulture are the main agricultural exports in Kenya. Export price of these commodities fluctuates mainly due to law of demand and supply. Other reasons include; quality of goods and inflation effect on the dollar or other hard currencies. Further, farmers and their cooperative societies are affected by the local foreign exchange. The government and other stake holders require prior information on price trends for ease of planning. Thus it is important to forecast export prices of these commodities. The purpose of this study is to compare the forecasting performance of artificial neural network (ANN) model and a SARIMA model in export price of tea in Kenya. Secondary data was obtained from Kenya National Bureau of Statistics (KNBS). A total of 185 monthly export prices were obtained. A three layer feed-forward artificial neural network was trained using 70% of the data. The ANN model obtained was used to predict export prices for the remaining 30% of the data. SARIMA model was also used to predict export prices for the same duration. Forecasting performance was evaluated using Root mean squared errors (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). ANN demonstrated a superior performance over SARIMA model. The authors' ANN has high performance compared to SARIMA and can accurately predict export price of tea.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - Modeling Export Price of Tea in Kenya: Comparison of Artificial Neural Network and Seasonal Autoregressive Integrated Moving Average
    AU  - Mbiriri Ikonya
    AU  - Peter Mwita
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    DO  - 10.11648/j.ajtas.20140306.16
    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  - 211
    EP  - 216
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20140306.16
    AB  - Agriculture sector is a key driver of economic growth in Kenya. It remains the main source of livelihood for the majority of the Kenyan people. Tea, coffee, and horticulture are the main agricultural exports in Kenya. Export price of these commodities fluctuates mainly due to law of demand and supply. Other reasons include; quality of goods and inflation effect on the dollar or other hard currencies. Further, farmers and their cooperative societies are affected by the local foreign exchange. The government and other stake holders require prior information on price trends for ease of planning. Thus it is important to forecast export prices of these commodities. The purpose of this study is to compare the forecasting performance of artificial neural network (ANN) model and a SARIMA model in export price of tea in Kenya. Secondary data was obtained from Kenya National Bureau of Statistics (KNBS). A total of 185 monthly export prices were obtained. A three layer feed-forward artificial neural network was trained using 70% of the data. The ANN model obtained was used to predict export prices for the remaining 30% of the data. SARIMA model was also used to predict export prices for the same duration. Forecasting performance was evaluated using Root mean squared errors (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). ANN demonstrated a superior performance over SARIMA model. The authors' ANN has high performance compared to SARIMA and can accurately predict export price of tea.
    VL  - 3
    IS  - 6
    ER  - 

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Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUA T), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUA T), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUA T), Nairobi, Kenya

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