TY - GEN
T1 - Fishery forecasting based on singular spectrum analysis combined with bivariate regression
AU - Barba, Lida
AU - Rodríguez, Nibaldo
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - The fishery of anchovy and sardine has a great importance in the economy of Chile; they are important resources used for internal consumption and for export. The forecasting based on historical time series is a fishery planning tool. In this paper is presented the forecasting of anchovy and sardine by means of the monthly catches in the Chilean northern coast (18°S – 24°S), during the period January 1976 to December 2007. The forecasting strategy is presented in two stages: preprocessing and prediction. In the first stage the Singular Spectrum Analysis (SSA) technique is applied to extract the components interannual and annual of the time series. In the second stage the Bivariate Regression (BVR) is implemented to predict the extracted components. The results evaluated with the efficiency metrics show a high prediction accuracy of the strategy based on SSA and BVR. Besides, the results are compared with a conventional nonlinear prediction based on an Autoregressive Neural Network (ANN) with Levenberg-Marquardt; it was demonstrated the improvement in the prediction accuracy by using the proposed strategy SSA-BVR with regard to the results obtained with the ANN.
AB - The fishery of anchovy and sardine has a great importance in the economy of Chile; they are important resources used for internal consumption and for export. The forecasting based on historical time series is a fishery planning tool. In this paper is presented the forecasting of anchovy and sardine by means of the monthly catches in the Chilean northern coast (18°S – 24°S), during the period January 1976 to December 2007. The forecasting strategy is presented in two stages: preprocessing and prediction. In the first stage the Singular Spectrum Analysis (SSA) technique is applied to extract the components interannual and annual of the time series. In the second stage the Bivariate Regression (BVR) is implemented to predict the extracted components. The results evaluated with the efficiency metrics show a high prediction accuracy of the strategy based on SSA and BVR. Besides, the results are compared with a conventional nonlinear prediction based on an Autoregressive Neural Network (ANN) with Levenberg-Marquardt; it was demonstrated the improvement in the prediction accuracy by using the proposed strategy SSA-BVR with regard to the results obtained with the ANN.
UR - http://www.scopus.com/inward/record.url?scp=84952690751&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27101-9_37
DO - 10.1007/978-3-319-27101-9_37
M3 - Conference contribution
AN - SCOPUS:84952690751
SN - 9783319271002
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 487
EP - 497
BT - Advances in Artificial Intelligence and Its Applications - 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, Proceedings
A2 - Alcántara, Oscar Herrera
A2 - Lagunas, Obdulia Pichardo
A2 - Figueroa, Gustavo Arroyo
PB - Springer Verlag
T2 - 14th Mexican International Conference on Artificial Intelligence, MICAI 2015
Y2 - 25 October 2015 through 31 October 2015
ER -
