Wavelet based autoregressive RBF network for sardines catches forecasting

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

This paper deals with forecasting of monthly sardines catches in north area of Chile. The forecasting model is based on un-decimated stationary wavelet transform (SWT) combined with radial basis function (RBF) neural network and linear autoregressive (AR) model. The original monthly sardines catches data are decomposed into two sub-series employing 1-level SWT and the appropriate subseries are used as inputs to the (RBF+AR) model to forecast 1-month ahead monthly sardines catches. The forecaster's parameters are estimated by using a hybrid algorithm based on the least square (LS) method and Levenberg Marquardt (LM) algorithm. The forecasting performance based on Hybrid (LS+LM) algorithm based was evaluated using determination coefficient and showed that a 99% of the explained variance was captured with a reduced parsimony and high accuracy.

Idioma originalInglés
Título de la publicación alojadaProceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
Páginas808-811
Número de páginas4
DOI
EstadoPublicada - 2008
Publicado de forma externa
Evento3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 - Busan, República de Corea
Duración: 11 nov. 200813 nov. 2008

Serie de la publicación

NombreProceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
Volumen2

Conferencia

Conferencia3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
País/TerritorioRepública de Corea
CiudadBusan
Período11/11/0813/11/08

Huella

Profundice en los temas de investigación de 'Wavelet based autoregressive RBF network for sardines catches forecasting'. En conjunto forman una huella única.

Citar esto