Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models

Cecilia Mbithe Titus; Anthony Wanjoya; Thomas Mageto

doi:doi:10.11648/j.ijdsa.20210701.11

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Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto

Published in International Journal of Data Science and Analysis (Volume 7, Issue 1)

Received: 26 January 2021 Accepted: 6 February 2021 Published: 10 February 2021

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Abstract

Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.

Published in	International Journal of Data Science and Analysis (Volume 7, Issue 1)
DOI	10.11648/j.ijdsa.20210701.11
Page(s)	1-7
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), 2021. Published by Science Publishing Group

Keywords

Poultry Farming, Auto-Regression, Fractional Integration, Long-Memory, Augmented Dickey Fuller (ADF) Test, Random Mean Squared Error (RMSE)

References

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[9]	Shakeel, N. & Masood, A. K. (2014). Modeling and Forecasting of Beef, Mutton, Poultry Meat and Total Meat Production of Pakistan for Year 2020 by using Time Series ARIMA Models. European Scientific Journal, 3 (special issue).
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[14]	Abdul, S. M., Bonsu, F. R. K., Abunyuwah, I. & Serekye, A. Y. (2020). Relative Economic Value Estimates of Guinea Fowls (Numida meleagris) Production Traits. World Journal of Advanced Research and Reviews, 7 (1); 273-281. doi: 10.30574/wjarr.2020.7.1.0265.
[15]	Dzungwe, J. T., Gwaza, D. S. & Egahi, J. O. (2018). Statistical Modeling of Body Weight and Body Linear Measurements of the French Broiler Guinea Fowl in the Humid Tropics of Nigeria. Poultry, Fisheries and Wildlife Sciences, 6 (2); 1-4. doi: 10.4172/2375-446X. 1000197.

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APA Style

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. (2021). Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. International Journal of Data Science and Analysis, 7(1), 1-7. https://doi.org/10.11648/j.ijdsa.20210701.11

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ACS Style

Cecilia Mbithe Titus; Anthony Wanjoya; Thomas Mageto. Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. Int. J. Data Sci. Anal. 2021, 7(1), 1-7. doi: 10.11648/j.ijdsa.20210701.11

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AMA Style

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. Int J Data Sci Anal. 2021;7(1):1-7. doi: 10.11648/j.ijdsa.20210701.11

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@article{10.11648/j.ijdsa.20210701.11,
  author = {Cecilia Mbithe Titus and Anthony Wanjoya and Thomas Mageto},
  title = {Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {1},
  pages = {1-7},
  doi = {10.11648/j.ijdsa.20210701.11},
  url = {https://doi.org/10.11648/j.ijdsa.20210701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210701.11},
  abstract = {Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.},
 year = {2021}
}

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TY  - JOUR
T1  - Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models
AU  - Cecilia Mbithe Titus
AU  - Anthony Wanjoya
AU  - Thomas Mageto
Y1  - 2021/02/10
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210701.11
DO  - 10.11648/j.ijdsa.20210701.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210701.11
AB  - Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.
VL  - 7
IS  - 1
ER  -

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Author Information

Cecilia Mbithe Titus

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

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Thomas Mageto

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

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Plain Text BibTeX RIS

APA Style

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. (2021). Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. International Journal of Data Science and Analysis, 7(1), 1-7. https://doi.org/10.11648/j.ijdsa.20210701.11

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ACS Style

Cecilia Mbithe Titus; Anthony Wanjoya; Thomas Mageto. Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. Int. J. Data Sci. Anal. 2021, 7(1), 1-7. doi: 10.11648/j.ijdsa.20210701.11

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AMA Style

Cecilia Mbithe Titus, Anthony Wanjoya, Thomas Mageto. Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. Int J Data Sci Anal. 2021;7(1):1-7. doi: 10.11648/j.ijdsa.20210701.11

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@article{10.11648/j.ijdsa.20210701.11,
  author = {Cecilia Mbithe Titus and Anthony Wanjoya and Thomas Mageto},
  title = {Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {1},
  pages = {1-7},
  doi = {10.11648/j.ijdsa.20210701.11},
  url = {https://doi.org/10.11648/j.ijdsa.20210701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210701.11},
  abstract = {Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models
AU  - Cecilia Mbithe Titus
AU  - Anthony Wanjoya
AU  - Thomas Mageto
Y1  - 2021/02/10
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210701.11
DO  - 10.11648/j.ijdsa.20210701.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210701.11
AB  - Commercial farming of Guinea Fowls is at its infant stages and is generating a lot of interest for farmers in Kenya. This, coupled with an increased demand for poultry products in the Kenyan market in the recent past, calls for the rearing of the guinea fowls which are birds reared for meat and partly for eggs. In order to have an efficient production of poultry products for this type of poultry farming, there is need for an efficient modeling using sound statistical methodologies. It’s in this regard that the study modeled Guinea Fowl production in Kenya using the Univariate Auto-Regressive Integrated Moving Average (ARIMA) and the Auto-Regressive Fractional Integrated Moving Average (ARFIMA) models. Yearly guinea fowl production data for the period of 2010 to 2019 obtained from Food and Agricultural Organization (FAO-Kenya) was used in the study in which the Augmented Dickey Fuller (ADF) test was used to check for stationarity while the Hurst Exponent was used to test the long-memory property of the series. The ARIMA and ARFIMA models gave a better fit to the data and were used to forecast Guinea Fowl Weights. Fitted model forecast were evaluated via the Random Mean Squared Error (RMSE) in which the ARFIMA model was found to give a better forecast of the Guinea Fowl weights compared to the ARIMA model.
VL  - 7
IS  - 1
ER  -

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