Journal of Applied Mathematics

Multi-Step-Ahead Forecasting Model for Monthly Anchovy Catches Based on Wavelet Analysis

Nibaldo Rodríguez, Claudio Cubillos, and José-Miguel Rubio

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

This paper presents a p -step-ahead forecasting strategy based on two stages to improve pelagic fish-catch time-series modeling by considering annual and interannual fluctuations for northern Chile (18°S–24°S). In the first stage, the stationary wavelet transform is used to separate the raw time series into an annual component and an interannual component, whereas the periodicities of each component are obtained using the Morlet wavelet power spectrum. In the second stage, a linear autoregressive model is constructed to predict each component and the unknown p -next values are forecasted by the addition of the two predicted components. We demonstrate the utility of the proposed forecasting model on monthly anchovy-catches time series for periods from January 1963 to December 2007. Empirical results obtained for 10-month-ahead forecasting showed the effectiveness of the proposed wavelet autoregressive strategy.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 798464, 8 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305552

Digital Object Identifier
doi:10.1155/2014/798464

Citation

Rodríguez, Nibaldo; Cubillos, Claudio; Rubio, José-Miguel. Multi-Step-Ahead Forecasting Model for Monthly Anchovy Catches Based on Wavelet Analysis. J. Appl. Math. 2014 (2014), Article ID 798464, 8 pages. doi:10.1155/2014/798464. https://projecteuclid.org/euclid.jam/1425305552


Export citation

References

  • L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. LI, “Iterated time series prediction with multiple support vector regression models,” Neurocomputing, vol. 99, pp. 411–422, 2013.
  • C. Hamzaçebi, D. Akay, and F. Kutay, “Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting,” Expert Systems with Applications, vol. 36, no. 2, pp. 3839–3844, 2009.
  • A. Sorjamaa, J. Hao, N. Reyhani, Y. Ji, and A. Lendasse, “Methodology for long-term prediction of time series,” Neurocomputing, vol. 70, no. 16–18, pp. 2861–2869, 2007.
  • H. Cheng, P.-N. Tan, J. Gao, and J. Scripps, “Multistep-ahead time series prediction,” in Advances in Knowledge Discovery and Data Mining, W.-K. Ng, M. Kitsuregawa, J. Li, and K. Chang, Eds., vol. 3918 of Lecture Notes in Computer Science, pp. 765–774, Springer, Berlin, Germany, 2006.
  • D. M. Kline, “Methods for multi-step time series forecasting with neural networks,” in Neural Networks in Business Forecasting, G. P. Zhang, Ed., pp. 226–250, Information Science, Hershey, Pa, USA, 2004.
  • G. C. Tiao and R. S. Tsay, “Some advances in non-linear and adaptive modelling in time-series,” Journal of Forecasting, vol. 13, no. 2, pp. 109–131, 1994.
  • 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.
  • K. I. Stergiou and E. D. Christou, “Modelling and forecasting annual fisheries catches: comparison of regression, univariate and multivariate time series methods,” Fisheries Research, vol. 25, no. 2, pp. 105–138, 1996.
  • K. I. Stergiou, “Short-term fisheries forecasting: comparison of smoothing, ARIMA and regression techniques,” Journal of Applied Ichthyology, vol. 7, no. 4, pp. 193–204, 1991.
  • 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.
  • E. Yáñez, F. Plaza, J. C. Gutiérrez-Estrada et al., “Anchovy (Engraulis ringens) and sardine (Sardinops sagax) abundance forecast off northern Chile: a multivariate ecosystemic neural network approach,” Progress in Oceanography, vol. 87, no. 1–4, pp. 242–250, 2010.
  • N. A. Shrivastava and B. K. Panigrahi, “A hybrid wavelet-ELM based short term price forecasting for electricity markets,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 41–50, 2014.
  • N. Amjady and F. Keynia, “Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method,” International Journal of Electrical Power & Energy Systems, vol. 30, no. 9, pp. 533–546, 2008.
  • G. Nason and B. Silverman, “The stationary wavelet transform and some statistical applications,” in Wavelets and Statistics, vol. 103 of Lecture Notes in Statistics, pp. 281–299, Springer, New York, NY, USA, 1995.
  • R. R. Coifman and D. L. Donoho, “Translation-invariant de-noising,” in Wavelets and Statistics, vol. 103 of Lecture Notes in Statistics, pp. 125–150, Springer, New York, NY, USA, 1995.
  • J.-C. Pesquet, H. Krim, and H. Carfantan, “Time-invariant orthonormal wavelet representations,” IEEE Transactions on Signal Processing, vol. 44, no. 8, pp. 1964–1970, 1996.
  • D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, Cambridge, UK, 2000.
  • S. Lahmiri, “Forecasting direction of the S-P500 movement using wavelet transform and support vector machines,” International Journal of Strategic Decision Sciences, vol. 4, no. 1, pp. 78–88, 2013.
  • T.-J. Hsieh, H.-F. Hsiao, and W.-C. Yeh, “Forecasting stock markets using wavelet transforms and recurrent neural networks: an integrated system based on artificial bee colony algorithm,” Applied Soft Computing, vol. 11, no. 2, pp. 2510–2525, 2011.
  • 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.
  • M. Hidalgo, T. Rouyer, J. C. Molinero et al., “Synergistic effects of fishing-induced demographic changes and climate variation on fish population dynamics,” Marine Ecology Progress Series, vol. 426, pp. 1–12, 2011.
  • B. Cazelles, M. Chavez, D. Berteaux et al., “Wavelet analysis of ecological time series,” Oecologia, vol. 156, no. 2, pp. 287–304, 2008.
  • T. Rouyer, J.-M. Fromentin, N. C. Stenseth, and B. Cazelles, “Analysing multiple time series and extending significance testing in wavelet analysis,” Marine Ecology Progress Series, vol. 359, pp. 11–23, 2008.
  • C.-H. Hsieh, C.-S. Chen, T.-S. Chiu et al., “Time series analyses reveal transient relationships between abundance of larval anchovy and environmental variables in the coastal waters southwest of Taiwan,” Fisheries Oceanography, vol. 18, no. 2, pp. 102–117, 2009.
  • I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, Pa, USA, 1992.
  • S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, San Diego, Calif, USA, 1998.
  • C. Torrence and G. P. Compo, “A practical guide to wavelet analysis,” Bulletin of the American Meteorological Society, vol. 79, no. 1, pp. 61–78, 1998.
  • A. Grinsted, J. C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to geophysical times series,” Nonlinear Processes in Geophysics, vol. 11, no. 5-6, pp. 561–566, 2004.
  • D. Serre, Matrices: Theory and Applications, Springer, New York, NY, USA, 2002.
  • A. Quetglas, F. Ordines, M. Hidalgo et al., “Synchronous combined effects of fishing and climate within a demersal community,” ICES Journal of Marine Science, vol. 70, no. 2, pp. 319–328, 2013.
  • S. E. Lluch-Cota, A. Parés-Sierra, V. O. Magaña-Rueda et al., “Changing climate in the Gulf of California,” Progress in Oceanography, vol. 87, no. 1–4, pp. 114–126, 2010. \endinput