Wavelet based autoregressive RBF network for sardines catches forecasting

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
Pages808-811
Number of pages4
DOIs
StatePublished - 29 Dec 2008
Event3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 - Busan, Korea, Republic of
Duration: 11 Nov 200813 Nov 2008

Publication series

NameProceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
Volume2

Conference

Conference3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
CountryKorea, Republic of
CityBusan
Period11/11/0813/11/08

Fingerprint Dive into the research topics of 'Wavelet based autoregressive RBF network for sardines catches forecasting'. Together they form a unique fingerprint.

Cite this