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

Mbiriri Ikonya; Peter Mwita; Anthony Wanjoya

doi:doi:10.11648/j.ajtas.20140306.16

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

Mbiriri Ikonya, Peter Mwita, Anthony Wanjoya

Published in American Journal of Theoretical and Applied Statistics (Volume 3, Issue 6)

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

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[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.
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[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.
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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
AU - Anthony Wanjoya
Y1 - 2014/12/19
PY - 2014
N1 - https://doi.org/10.11648/j.ajtas.20140306.16
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

Mbiriri Ikonya

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

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

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

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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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