@inproceedings{d4e42723388a4f65ba53c49790ad28eb,
title = "Wavelet based autoregressive RBF network for sardines catches forecasting",
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.",
author = "Nibaldo Rodr{\'i}guez and Eleuterio Ya{\~n}ez and Broderick Crawford",
year = "2008",
doi = "10.1109/ICCIT.2008.357",
language = "English",
isbn = "9780769534077",
series = "Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008",
pages = "808--811",
booktitle = "Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008",
note = "null ; Conference date: 11-11-2008 Through 13-11-2008",
}