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Mathematical Problems in Engineering
Volume 2015, Article ID 969450, 9 pages
http://dx.doi.org/10.1155/2015/969450
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.

Linked References

  1. J. C. Gutiérrez-Estrada, C. Silva, E. Yáñez, N. Rodríguez, and I. Pulido-Calvo, “Monthly catch forecasting of anchovy Engraulis ringens in the North area of Chile: non-linear univariate approach,” Fisheries Research, vol. 86, no. 2-3, pp. 188–200, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. K. I. Stergiou, E. D. Christou, and G. Petrakis, “Modelling and forecasting monthly fisheries catches: comparison of regression, univariate and multivariate time series methods,” Fisheries Research, vol. 29, no. 1, pp. 55–95, 1997. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Lloret, J. Lleonart, and I. Solé, “Time series modelling of landings in Northwest Mediterranean Sea,” ICES Journal of Marine Science, vol. 57, no. 1, pp. 171–184, 2000. View at Publisher · View at Google Scholar · View at Scopus
  4. G. J. Pierce and P. R. Boyle, “Empirical modelling of interannual trends in abundance of squid (Loligo forbesi) in Scottish waters,” Fisheries Research, vol. 59, no. 3, pp. 305–326, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Koutroumanidis, L. Iliadis, and G. K. Sylaios, “Time-series modeling of fishery landings using ARIMA models and fuzzy expected intervals software,” Environmental Modelling and Software, vol. 21, no. 12, pp. 1711–1721, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Zhou, “Application of artificial neural networks for forecasting Salmon escapement,” North American Journal of Fisheries Management, vol. 23, no. 1, pp. 48–59, 2003. View at Publisher · View at Google Scholar
  7. S. Georgakarakos, D. Koutsoubas, and V. Valavanis, “Time series analysis and forecasting techniques applied on loliginid and ommastrephid landings in Greek waters,” Fisheries Research, vol. 78, no. 1, pp. 55–71, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. M. M. A. Abdelaal and E. F. Aziz, “Modeling and forecasting fish production using univariate and multivariate ARIMA models,” Far East Journal of Theoretical Statistics, vol. 41, no. 1, pp. 1–26, 2012. View at Google Scholar
  9. H. Y. Bako, M. S. Rusiman, I. L. Kane, and H. M. Matias-Peralta, “Predictive modeling of pelagic fish catch in Malaysia using seasonal ARIMA models,” Algriture Forestry and Fisheries, vol. 2, no. 3, pp. 136–140, 2013. View at Google Scholar
  10. M. Khashei and M. Bijari, “An artificial neural network (p, d, q) model for timeseries forecasting,” Expert Systems with Applications, vol. 37, no. 1, pp. 479–489, 2010. View at Google Scholar
  11. M. Khashei and M. Bijari, “A new hybrid methodology for nonlinear time series forecasting,” Modelling and Simulation in Engineering, vol. 2011, Article ID 379121, 5 pages, 2011. View at Publisher · View at Google Scholar
  12. S. G. Makridakis, S. C. Wheelright, and V. E. McGee, Forecasting: Methods and Applications, Wiley, New York, NY, USA, 3rd edition, 1998.
  13. C. Torrence and G. P. Compo, “A practicle guide to wavelet analysis,” Bulletin of the American Meteorological Society, vol. 79, no. 1, pp. 61–78, 1998. View at Google Scholar · View at Scopus
  14. B.-L. Zhang, R. Coggins, M. A. Jabri, D. Dersch, and B. Flower, “Multiresolution forecasting for futures trading using wavelet decompositions,” IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 765–775, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Labat, R. Ababou, and A. Mangin, “Rainfall-runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses,” Journal of Hydrology, vol. 238, no. 3-4, pp. 149–178, 2000. View at Publisher · View at Google Scholar · View at Scopus
  16. O. Kisi, “Wavelet regression model for short-term streamflow forecasting,” Journal of Hydrology, vol. 389, no. 3-4, pp. 344–353, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Wang and J. Ding, “Wavelet network model and its application to the prediction of the hydrology,” Nature and Science, vol. 1, no. 1, pp. 67–71, 2003. View at Google Scholar
  18. X. Guo, L. Sun, G. Li, and S. Wang, “A hybrid wavelet analysis and support vector machines in forecasting development of manufacturing,” Expert Systems with Applications, vol. 35, no. 1-2, pp. 415–422, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. V. Nourani, M. T. Alami, and M. H. Aminfar, “A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation,” Engineering Applications of Artificial Intelligence, vol. 22, no. 3, pp. 466–472, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. T.-M. Choi, Y. Yu, and K.-F. Au, “A hybrid SARIMA wavelet transform method for sales forecasting,” Decision Support Systems, vol. 51, no. 1, pp. 130–140, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. Z. Tan, J. Zhang, J. Wang, and J. Xu, “Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models,” Applied Energy, vol. 87, no. 11, pp. 3606–3610, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Adamowski and K. Sun, “Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds,” Journal of Hydrology, vol. 390, no. 1-2, pp. 85–91, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Adamowski, H. F. Chan, S. O. Prasher, and V. N. Sharda, “Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data,” Journal of Hydroinformatics, vol. 14, no. 3, pp. 731–744, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. G. E. Box and G. M. Jenkins, Time Series Analysis. Forecasting and Control, Holden-Day, San Francisco, Calif, USA, 1970. View at MathSciNet
  25. S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989. View at Publisher · View at Google Scholar · View at Scopus