Predictive modeling of pelagic fish catch in Malaysia using seasonal ARIMA models
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...
| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Science Publishing Group
2013
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| Online Access: | doi:10.11648/j.aff.20130203.13 doi:10.11648/j.aff.20130203.13 http://eprints.uthm.edu.my/9374/1/J1867_100d4e8b78049ffa439f8d5c1f8b9d3a.pdf |
| Summary: | 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. |
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