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Arima and Integrated Arfima Models for Forecasting Annual Demersal and Pelagic Marine Fish Production in Malaysia

Shitan, Mahendran and Wee, Pauline Mah Jin and Lim, Ying Chin and Lim, Ying Siew (2008) Arima and Integrated Arfima Models for Forecasting Annual Demersal and Pelagic Marine Fish Production in Malaysia. Malaysian Journal of Mathematical Sciences, 2 (2). pp. 41-54. ISSN 1823-8343

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Abstract

The seas surrounding Malaysia provide a rich source of marine fisheries. The fisheries industry is an important economic earner and the total marine fish production has increased drastically from 242,900 metric tonnes in 1970 to around a million metric tonnes in the year 2000. Since fisheries resources are renewable, proper management issues should be taken to manage these fisheries resources. From the management point of view, fish forecasting is a very important tool for fisheries managers and scientists to enable them to decide on sustainable management issues. Time series models have been used to forecast various phenomena in many fields. In a previous research by Mahendran Shitan et. al. (2004), the maximum likelihood and bootstrap method were used to forecast the total Malaysian marine fish production. Marine fish can be sub-classified as demersal marine fish and pelagic marine fish and it would be interesting to forecast the individual composition of these categories. Therefore, in this research we fit time series models to forecast the demersal and pelagic marine fish production using ARIMA and integrated ARFIMA models and make predictions of each category. Our results indicate that the ARIMA models appear to be the better models and the forecasted amounts for the year 2011 are approximately 373,370 and 666,460 metric tonnes for the demersal and pelagic marine fish, respectively.

Item Type:Article
Keyword:Arima, Pelagic Marine Fish
Faculty or Institute:Institute for Mathematical Research
Publisher:UPM Press
ID Code:12597
Deposited By: kmportal
Deposited On:09 Jun 2011 17:27
Last Modified:27 May 2013 15:52

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