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Circulant singular value decomposition combined with a conventional neural network to improve the hake catches prediction

Abstract:

This paper presents the one-step ahead forecasting of time series based on Singular Value Decomposition of a circulant trajectory matrix combined with the conventional non linear prediction method. The catches of a fishery resource was used to evaluate the proposal, this is due to the great importance of this resource in the economy of a country, and its high variability presents difficulties in the forecasting; the catches of hakes from January 1963 to December 2008 along the Chilean coast (30° S–40°S) are the application data. The forecasting strategy is presented in two stages: preprocessing and prediction. In the first stage the Singular Value Decomposition of a circulant matriz (CSVD) resultant of the mapping time series is applied to extract the components, after the decomposition and grouping, the components interannual and annual were obtained. In the second stage a conventional Artificial Neural Network (ANN) is implemented to predict the extracted components. The results evaluation shows a high prediction accuracy through the strategy based on the combination CSVD-ANN. Besides, the results were compared with the conventional nonlinear prediction based on an Autoregressive Neural Network. The improvement in the prediction accuracy by using the proposed decomposition strategy was demonstrated.