Title :
Wavelet Based Autoregressive RBF Network For Sardines Catches Forecasting
Author :
Rodriguez, Nibaldo ; Yaez, E. ; Crawford, Broderick
Author_Institution :
Pontificia Univ. Catolica de Valparaiso, Valparaiso
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.
Keywords :
agricultural products; aquaculture; autoregressive processes; economic forecasting; fishing industry; least squares approximations; radial basis function networks; wavelet transforms; Levenberg Marquardt algorithm; determination coefficient; least square method; linear autoregressive model; radial basis function neural network; sardines catches forecasting; stationary wavelet transform; Aquaculture; Discrete wavelet transforms; Information technology; Least squares approximation; Neural networks; Noise reduction; Phase estimation; Predictive models; Radial basis function networks; Technology forecasting; Neural Network; forecasting;
Conference_Titel :
Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third International Conference on
Conference_Location :
Busan
Print_ISBN :
978-0-7695-3407-7
DOI :
10.1109/ICCIT.2008.357
