Bi-variate Wavelet Autoregressive Model for Multi-step-ahead Forecasting of Fish Catches

 

Nibaldo Rodriguez¹ and Lida Barba²

 

¹ School of Computer Engineering at the Pontificia Universidad Católica de Valparaíso, Av. Brasil 2241, Chile (e-mail: nibaldo.rodriguez@ucv.cl).

² School of Computer Engineering at the Universidad Nacional de Chimborazo, Av. Antonio Jose de Sucre, Riobamba, Ecuador (e-mail: lbarba@unach.edu.ec).

 

Manuscript received on May 28, 2015
Accepted for publication on July 30, 2015
Published on October 15, 2015

 

Abstract

This paper proposes a hybrid multi-step-ahead forecasting model based on two stages to improve monthly pelagic fish-catch time-series modeling. In the first stage, the stationary wavelet transform is used to separate the raw time series into a high frequency (HF) component and a low frequency (LF) component, whereas the periodicities of each time series is obtained by using the Fourier power spectrum. In the second stage, both the HF and LF components are the inputs into a bi-variate autoregressive model to predict the original time series. We demonstrate the utility of the proposed forecasting model on monthly sardines catches time-series of the coastal zone of Chile for periods from January 1949 to December 2011. Empirical results obtained for 12-month ahead forecasting showed the effectiveness of the proposed hybrid forecasting strategy.

Keywords: Wavelet analysis, bi-variate regression, forecasting model.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

Acknowledgment

This research was partially supported by the Chilean National Science Fund through the project Fondecyt-Regular 1131105 and by the project DI-Regular 037.442/2015 of the Pontificia Universidad Católica de Valparaíso.

 

REFERENCES

[1] S. KI., "Prediction of the mullidae fishery in the easterm mediterranean 24 months in advance," Fisheries Research, vol. 9, pp. 67-74, 1996.         [ Links ]

[2] S. K.I. and C. E.D., "Modelling and forecasting annual fisheries catches: comparison of regression, univariate and multivariate time series methods," Fisheries Research, vol. 25, pp. 105-138, 1996.         [ Links ]

[3] J. C. Gutierrez, S. C., Y. E., R. N., and P. I., "Monthly catch forecasting of anchovy engraulis ringens in the north area of chile: Nonlinear univariate approach," Fisheries Research, vol. 86, no. 188-200, 2007.         [ Links ]

[4] S. P. Garcia, L. B. DeLancey, J. S. Almeida, and R. W. Chapman, "Ecoforecasting in real time for commercial fisheries: The Atlantic white shrimp as a case study," Marine Biology, vol. 152, pp. 15-24, 2007.         [ Links ]

[5] J. F. Adamowski, "Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis," Journal of Hydrology, vol. 353, no. 3-4, pp. 247-266, 2008.         [ Links ]

[6] O. Kisi, "Stream flow forecasting using neuro-wavelet technique," Hydrological Processes, vol. 22, no. 20, pp. 4142-4152, 2008.         [ Links ]

[7] N. Amjady and F. Keyniaa, "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, pp. 533-546, 2008.         [ Links ]

[8] B.-L. Z., R. C., M. A. J., D. D., and B. F., "Multiresolution forecasting for futures trading using wavelet decompositions," IEEE Trans. on neural networks, vol. 12, no. 4, pp. 765-775, 2001.         [ Links ]

[9] R. Coifman and D. L. Donoho, "Translation-invariant denoising, wavelets and statistics," Springer Lecture Notes in Statistics, vol. 103, pp. 125-150, 1995.         [ Links ]

[10] G. Nason and B. Silverman, "The stationary wavelet transform and some statistical applications, wavelets and statistics," Springer Lecture Notes in Statistics, vol. 103, pp. 281-300, 1995.         [ Links ]

[11] J.-C. Pesquet, H. Krim, and H. Carfantan, "Time-invariant orthonormal wavelet representations," IEEE Trans. on Signal Processing, vol. 44, no. 8, pp. 1964-1970, 1996.         [ Links ]

[12] D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis. Cambridge, England: Cambridge University Press, 2000.         [ Links ]

[13] D. Serre, Matrices: Theory and Applications. Springer, New York, NY, USA, 2002.         [ Links ]

[14] M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the marquardt algorithm," IEEE transactions on neural networks, vol. 5, no. 6, pp. 989-993, 1996.         [ Links ]