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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; ESSN: 2289-750X


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.

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Official URL or Download Paper: http://einspem.upm.edu.my/journal/volume2.2.php

Additional Metadata

Item Type: Article
Divisions: Institute for Mathematical Research
Faculty of Science
Publisher: Institute for Mathematical Research, Universiti Putra Malaysia
Keywords: ARIMA models; Fish forecasting; Marine fish; Integrated ARFIMA models
Depositing User: kmportal
Date Deposited: 09 Jun 2011 09:27
Last Modified: 02 Jun 2015 00:19
URI: http://psasir.upm.edu.my/id/eprint/12597
Statistic Details: View Download Statistic

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