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Mathematical Problems in Engineering
Volume 2015, Article ID 969450, 9 pages
Research Article

Fishery Landing Forecasting Using Wavelet-Based Autoregressive Integrated Moving Average Models

1Department of Science Mathematic, Faculty of Science, Universiti Teknologi Malaysia (UTM), 81310 Johor, Malaysia
2Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310 Johor, Malaysia

Received 26 August 2014; Revised 7 November 2014; Accepted 11 December 2014

Academic Editor: Erol Egrioglu

Copyright © 2015 Ani Shabri and Ruhaidah Samsudin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The accuracy of the wavelet-ARIMA (WA) model in monthly fishery landing forecasting is investigated in the study. In the first part of the study, the discrete wallet transform (DWT) is used to decompose fishery landing time series data. Then ARIMA, as a powerful forecasting tool, is implemented to predict each wavelet transform subseries components independently. Finally, the prediction results of the modeled subseries components are summed to formulate an ensemble forecast for the original fishery landing series. To assess the effectiveness of this model, monthly fishery landing recorded data from East Johor and Pahang states of Peninsular Malaysia have been used as a case study. The result of the study shows that the proposed model was found to provide more accurate fishery landing series forecasts than the individual ARIMA model.