TY - GEN
T1 - Circulant singular value decomposition combined with a conventional neural network to improve the hake catches prediction
AU - Barba, Lida
AU - Rodríguez, Nibaldo
AU - Barba, Diego
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84955296278&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-26832-3_34
DO - 10.1007/978-3-319-26832-3_34
M3 - Conference contribution
AN - SCOPUS:84955296278
SN - 9783319268316
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 359
EP - 369
BT - Mining Intelligence and Knowledge Exploration - 3rd International Conference, MIKE 2015, Proceedings
A2 - Prasath, Rajendra
A2 - Vuppala, Anil Kumar
A2 - Kathirvalavakumar, T.
PB - Springer Verlag
T2 - 3rd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015
Y2 - 9 December 2015 through 11 December 2015
ER -
