Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models

Hadiza Yakubu Bako; Mohd Saifullah Rusiman; Ibrahim Lawal Kane; Hazel Monica Matias-Peralta

doi:doi:10.11648/j.aff.20130203.13

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Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models

Hadiza Yakubu Bako, Mohd Saifullah Rusiman, Ibrahim Lawal Kane, Hazel Monica Matias-Peralta

Published in Agriculture, Forestry and Fisheries (Volume 2, Issue 3)

Received: 19 June 2013 Published: 10 July 2013

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Abstract

Fish catch prediction is an important problem in the fisheries sector and has a long history of research. The main goal of this paper is to create a model and make predictions using fish catch data of two fish species. Among the most effective and prominent approaches for analyzing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build Seasonal Autoregressive Integrated Moving Average (SARIMA) model for monthly catches of two fish species for a period of five years (2007 – 2011). The seasonal ARIMA (1, 1, 0)(0, 0, 1)12 and SARIMA (0, 1, 1) (0, 0, 1)12 models were found fit and confirmed by the Ljung-Box test and these models were used to forecast 5 months upcoming catches of Trichiurus lepturus (Ikan Selayor) and Amblygaster leiogaster (Tambun Beluru) fish species. The result will help decision makers to establish priorities in terms of fisheries management.

Published in	Agriculture, Forestry and Fisheries (Volume 2, Issue 3)
DOI	10.11648/j.aff.20130203.13
Page(s)	136-140
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), 2013. Published by Science Publishing Group

Keywords

Box-Jenkins, SARIMA, Ljung-Box, Fish Catches, Trichiurus lepturus, Amblygaster Leiogaster

References

[1]	Jabatan Perikanan Malaysia, "Annual Fisheries Statistics 2003 Jilid 1," 2003, Jabatan Perikanan Malaysia.
[2]	Efthymia, V . T, Christos, D. M and John, H. "Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models". Fisheries Science; 73: 5; 979–988, 2007
[3]	Stergiou, K.I., Christou, E.D. and Petrakis, G. "Modelling and forecasting monthly fisheries catches: comparison of regression, univariate and multivariate time series methods". Fish Res.; 29, 55–95, 1997.
[4]	Chi-Lu, S. and Su-Zan, Y. "ARIMA Modeling and Forecasting the Albacore Catch and CPUE of the Taiwanese Tuna Longline Fishery in the South Atlantic Ocean." http://sol.oc.ntu.edu.tw/aot/1997/362/E362D1e.html, 1998.
[5]	Georgakarakos,S., Koutsoubas, D. and Valavanis, V. "Time series analysis and forecasting techniques appliedon loliginid and ommastrephid landings in Greek waters". Fisheries Research 78, 55–71, 2006.
[6]	Lloret, J., Lleonart, J. and Sole, I. "Time series modeling of landings in Northwest Mediterranean Sea". Journal of Marine Science; 57, 171–184, 2000.
[7]	Defrancesco, E. and Samuele, T. "An activity-based decision support system to evaluate the economic viability of fisheries management tools for the small pelagic species in the northern Adriatic Sea". http://www.iamb.it/ share/img_new_medit_articoli/426_12defrancesco.pdf, 2012.
[8]	Shitan, M., Jin Wee P.M., Ying Chin, L. and Ying Siew, L. "Arima and Integrated Arfima Models for Forecasting Annual Demersal and Pelagic Marine Fish Production in Malaysia". Malaysian Journal of Mathematical Sciences, 2:2, 41-54, 2008. (http://www.dof.gov.my/en/fishery-statistics).
[9]	Trichiurussp.https://en.wikipedia.org/wiki//largeheadhairtail.
[10]	Reeach.kahaku.go.jp/zoology/fishes-ofAndaman-sea/content/ clu pedae/01.html
[11]	Fadhilah, Y. & Ibrahim, L. K. "Modeling Monthly Rainfall Time Series Using ETS State Space and SARIMA Models. International Journal of Current Research. Vol. 4, Issue, 09, 195-200, 2012a.
[12]	Box, G.E.P., Jenkins G.M. "Time series analysis: forecasting and control. Holden-Day, Boca Raton, 1976.
[13]	Giraitis, L., Leipus, R. and Philippe, A. "The Test for Stationarity versus Trends and Unit Roots Errors". Department of Economics, London School of Economics, Houghton Street, London WC2A 2AE, United Kingdom, 2-3, 2002.
[14]	Ljung, G.M., Box, G.E.P "On a measure of a lack of fit in time series models’’. Biometrika 65(2): 297-303. doi: 10.1093/biomet/65.2.297, 1978
[15]	Wang, W., Van Gelder, P. H. A. J. M., Vrijling, J. K., and Ma, J. "Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes", Nonlin. Processes Geophys., 12, 55–66, doi:10.5194/npg-12-55-2005, 2005.
[16]	Laurini M. P. and Portugal, S. P. (2003). Long memory in the R$/US$ exchange rate: A robust Analysis, Finance lab working paper 3.
[17]	Fadhilah, Y. & Ibrahim, L. K. "Volatility modeling of rainfall time series". Theor Appl Climatol. DOI 10.1007/s00704-012-0778-8, 2012b.

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APA Style

Hadiza Yakubu Bako, Mohd Saifullah Rusiman, Ibrahim Lawal Kane, Hazel Monica Matias-Peralta. (2013). Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models. Agriculture, Forestry and Fisheries, 2(3), 136-140. https://doi.org/10.11648/j.aff.20130203.13

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ACS Style

Hadiza Yakubu Bako; Mohd Saifullah Rusiman; Ibrahim Lawal Kane; Hazel Monica Matias-Peralta. Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models. Agric. For. Fish. 2013, 2(3), 136-140. doi: 10.11648/j.aff.20130203.13

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AMA Style

Hadiza Yakubu Bako, Mohd Saifullah Rusiman, Ibrahim Lawal Kane, Hazel Monica Matias-Peralta. Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models. Agric For Fish. 2013;2(3):136-140. doi: 10.11648/j.aff.20130203.13

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@article{10.11648/j.aff.20130203.13,
  author = {Hadiza Yakubu Bako and Mohd Saifullah Rusiman and Ibrahim Lawal Kane and Hazel Monica Matias-Peralta},
  title = {Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {2},
  number = {3},
  pages = {136-140},
  doi = {10.11648/j.aff.20130203.13},
  url = {https://doi.org/10.11648/j.aff.20130203.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20130203.13},
  abstract = {Fish catch prediction is an important problem in the fisheries sector and has a long history of research. The main goal of this paper is to create a model and make predictions using fish catch data of two fish species. Among the most effective and prominent approaches for analyzing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build Seasonal Autoregressive Integrated Moving Average (SARIMA) model for monthly catches of two fish species for a period of five years (2007 – 2011). The seasonal ARIMA (1, 1, 0)(0, 0, 1)12 and SARIMA (0, 1, 1) (0, 0, 1)12 models were found fit and confirmed by the Ljung-Box test  and these models were used to forecast 5 months upcoming catches of Trichiurus lepturus (Ikan Selayor) and Amblygaster leiogaster (Tambun Beluru) fish species. The result will help decision makers to establish priorities in terms of fisheries management.},
 year = {2013}
}

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TY  - JOUR
T1  - Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models
AU  - Hadiza Yakubu Bako
AU  - Mohd Saifullah Rusiman
AU  - Ibrahim Lawal Kane
AU  - Hazel Monica Matias-Peralta
Y1  - 2013/07/10
PY  - 2013
N1  - https://doi.org/10.11648/j.aff.20130203.13
DO  - 10.11648/j.aff.20130203.13
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 136
EP  - 140
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20130203.13
AB  - Fish catch prediction is an important problem in the fisheries sector and has a long history of research. The main goal of this paper is to create a model and make predictions using fish catch data of two fish species. Among the most effective and prominent approaches for analyzing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build Seasonal Autoregressive Integrated Moving Average (SARIMA) model for monthly catches of two fish species for a period of five years (2007 – 2011). The seasonal ARIMA (1, 1, 0)(0, 0, 1)12 and SARIMA (0, 1, 1) (0, 0, 1)12 models were found fit and confirmed by the Ljung-Box test  and these models were used to forecast 5 months upcoming catches of Trichiurus lepturus (Ikan Selayor) and Amblygaster leiogaster (Tambun Beluru) fish species. The result will help decision makers to establish priorities in terms of fisheries management.
VL  - 2
IS  - 3
ER  -

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Author Information

Hadiza Yakubu Bako

Department of Technology and Heritage, Faculty of Science, Technology and Human Development, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
Mohd Saifullah Rusiman

Department of Mathematics and Statistics, Faculty of Science, Technology and Human Development, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
Ibrahim Lawal Kane

Department of Mathematics and Computer Science, Umaru Musa Yar’adua University, 2218, Katsina State, Nigeria
Hazel Monica Matias-Peralta

Department of Technology and Heritage, Faculty of Science, Technology and Human Development, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

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Plain Text BibTeX RIS

APA Style

Hadiza Yakubu Bako, Mohd Saifullah Rusiman, Ibrahim Lawal Kane, Hazel Monica Matias-Peralta. (2013). Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models. Agriculture, Forestry and Fisheries, 2(3), 136-140. https://doi.org/10.11648/j.aff.20130203.13

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ACS Style

Hadiza Yakubu Bako; Mohd Saifullah Rusiman; Ibrahim Lawal Kane; Hazel Monica Matias-Peralta. Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models. Agric. For. Fish. 2013, 2(3), 136-140. doi: 10.11648/j.aff.20130203.13

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AMA Style

Hadiza Yakubu Bako, Mohd Saifullah Rusiman, Ibrahim Lawal Kane, Hazel Monica Matias-Peralta. Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models. Agric For Fish. 2013;2(3):136-140. doi: 10.11648/j.aff.20130203.13

Copy | Download

@article{10.11648/j.aff.20130203.13,
  author = {Hadiza Yakubu Bako and Mohd Saifullah Rusiman and Ibrahim Lawal Kane and Hazel Monica Matias-Peralta},
  title = {Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {2},
  number = {3},
  pages = {136-140},
  doi = {10.11648/j.aff.20130203.13},
  url = {https://doi.org/10.11648/j.aff.20130203.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20130203.13},
  abstract = {Fish catch prediction is an important problem in the fisheries sector and has a long history of research. The main goal of this paper is to create a model and make predictions using fish catch data of two fish species. Among the most effective and prominent approaches for analyzing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build Seasonal Autoregressive Integrated Moving Average (SARIMA) model for monthly catches of two fish species for a period of five years (2007 – 2011). The seasonal ARIMA (1, 1, 0)(0, 0, 1)12 and SARIMA (0, 1, 1) (0, 0, 1)12 models were found fit and confirmed by the Ljung-Box test  and these models were used to forecast 5 months upcoming catches of Trichiurus lepturus (Ikan Selayor) and Amblygaster leiogaster (Tambun Beluru) fish species. The result will help decision makers to establish priorities in terms of fisheries management.},
 year = {2013}
}

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TY  - JOUR
T1  - Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models
AU  - Hadiza Yakubu Bako
AU  - Mohd Saifullah Rusiman
AU  - Ibrahim Lawal Kane
AU  - Hazel Monica Matias-Peralta
Y1  - 2013/07/10
PY  - 2013
N1  - https://doi.org/10.11648/j.aff.20130203.13
DO  - 10.11648/j.aff.20130203.13
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 136
EP  - 140
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20130203.13
AB  - Fish catch prediction is an important problem in the fisheries sector and has a long history of research. The main goal of this paper is to create a model and make predictions using fish catch data of two fish species. Among the most effective and prominent approaches for analyzing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build Seasonal Autoregressive Integrated Moving Average (SARIMA) model for monthly catches of two fish species for a period of five years (2007 – 2011). The seasonal ARIMA (1, 1, 0)(0, 0, 1)12 and SARIMA (0, 1, 1) (0, 0, 1)12 models were found fit and confirmed by the Ljung-Box test  and these models were used to forecast 5 months upcoming catches of Trichiurus lepturus (Ikan Selayor) and Amblygaster leiogaster (Tambun Beluru) fish species. The result will help decision makers to establish priorities in terms of fisheries management.
VL  - 2
IS  - 3
ER  -

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