Abstract
A hybrid method to forecast 1-month ahead monthly anchovy catches in the north area of Chile is proposed in this paper. This method combined two techniques, stationary wavelet transform (SWT) and radial basis function neural network (RBFNN). The observed monthly anchovy catches data is decomposed into two subseries using 1-level SWT and the appropriate subseries are used as inputs to the RBFNN to forecast original anchovy catches time series. The RBFNN architecture is composed of linear and nonlinear weights, which are estimates using the least square method and Levenberg-Marquardt algorithm; respectively. Wavelet-RBFNN based forecasting performance was evaluated by comparing it with classical RBFNN model. The benchmark results shown that a 99% of the explained variance was captured with a reduced parsimony and high speed convergence.
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References
Stergiou, K.I.: Prediction of the Mullidae fishery in the easterm Mediterranean 24 months in advance. Fish. Res. 9, 67–74 (1996)
Stergiou, K.I., Christou, E.D.: Modelling and forecasting annual fisheries catches: comparison of regression, univariate and multivariate time series methods. Fish. Res. 25, 105–138 (1996)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Network 2(5), 359–366 (1989)
Gutierrez, J.C., Silva, C., Yañez, E., Rodriguez, N., Pulido, I.: Monthly catch forecasting of anchovy engraulis ringens in the north area of Chile: Nonlinear univariate approach. Fisheries Research 86, 188–200 (2007)
Hagan, M., Menhaj, M.: Training feed-forward networks with marquardt algorithm. IEEE Trans. Neural networks 5(6), 1134–1139 (1994)
Coifman, R.R., Donoho, D.L.: Translation-invariant denoising, Wavelets and Statistics. Springer Lecture Notes in Statistics (103), pp. 125–150 (1995)
Nason, G., Silverman, B.: The stationary wavelet transform and some statistical applications, Wavelets and Statistics. Springer Lecture Notes in Statistics (103), pp. 281–300 (1995)
Pesquet, J.-C., Krim, H., Carfantan, H.: Time-invariant orthonormal wavelet representations. IEEE Trans. on Signal Processing 44(8), 1964–1970 (1996)
Karayiannis, N.B.: Reformulated radial basis neural networks trained by gradient descent. IEEE Trans. Neural networks 10(3), 188–200 (1999)
Serre, D.: Matrices: Theory and applications. Springer, New York (2002)
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Rodriguez, N., Crawford, B., Castro, C., Yañez, E. (2008). Hybrid Wavelet-RBFNN Model for Monthly Anchovy Catches Forecasting. In: Dochev, D., Pistore, M., Traverso, P. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2008. Lecture Notes in Computer Science(), vol 5253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85776-1_29
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DOI: https://doi.org/10.1007/978-3-540-85776-1_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85775-4
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