Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile

NIBALDO   RODRIGUEZ AGURTO; Jose Rubio; Eleuterio Yañez

doi:10.1007/978-3-642-25085-9_78

Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile

NIBALDO RODRIGUEZ AGURTO, Jose Rubio, Eleuterio Yañez

SCHOOL OF COMPUTER ENGINEERING

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, we use multi-scale stationary wavelet decomposition technique combined with a linear autoregressive model for one-month-ahead monthly sardine catches forecasting off central southern Chile.The monthly sardine catches data were collected from the database of the National Marine Fisheries Service for the period between 1 January 1964 and 30 December 2008. The proposed forecasting strategy is to decompose the raw sardine catches data set into trend component and residual component by using multi-scale stationary wavelet transform. In wavelet domain, both the trend component and the residual component are independently predicted using a linear autoregressive model. Hence, proposed forecaster is the co-addition of two predicted components. We find that the proposed forecasting method achieves a 99% of the explained variance with a reduced parsimonious and high accuracy.

Original language	English
Title of host publication	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings
Pages	654-663
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-25085-9_78
State	Published - 28 Nov 2011
Event	16th Iberoamerican Congress on Pattern Recognition, CIARP 2011 - Pucon, Chile Duration: 15 Nov 2011 → 18 Nov 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7042 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Iberoamerican Congress on Pattern Recognition, CIARP 2011
Country	Chile
City	Pucon
Period	15/11/11 → 18/11/11

Keywords

autoregression
forecasting
wavelet decomposition

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

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RODRIGUEZ AGURTO, NIBALDO., Rubio, J., & Yañez, E. (2011). Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings (pp. 654-663). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7042 LNCS). https://doi.org/10.1007/978-3-642-25085-9_78

RODRIGUEZ AGURTO, NIBALDO ; Rubio, Jose ; Yañez, Eleuterio. / Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings. 2011. pp. 654-663 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we use multi-scale stationary wavelet decomposition technique combined with a linear autoregressive model for one-month-ahead monthly sardine catches forecasting off central southern Chile.The monthly sardine catches data were collected from the database of the National Marine Fisheries Service for the period between 1 January 1964 and 30 December 2008. The proposed forecasting strategy is to decompose the raw sardine catches data set into trend component and residual component by using multi-scale stationary wavelet transform. In wavelet domain, both the trend component and the residual component are independently predicted using a linear autoregressive model. Hence, proposed forecaster is the co-addition of two predicted components. We find that the proposed forecasting method achieves a 99% of the explained variance with a reduced parsimonious and high accuracy.",

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author = "{RODRIGUEZ AGURTO}, NIBALDO and Jose Rubio and Eleuterio Ya{\~n}ez",

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RODRIGUEZ AGURTO, NIBALDO, Rubio, J & Yañez, E 2011, Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile. in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7042 LNCS, pp. 654-663, 16th Iberoamerican Congress on Pattern Recognition, CIARP 2011, Pucon, Chile, 15/11/11. https://doi.org/10.1007/978-3-642-25085-9_78

Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile. / RODRIGUEZ AGURTO, NIBALDO ; Rubio, Jose; Yañez, Eleuterio.

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings. 2011. p. 654-663 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7042 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile

AU - RODRIGUEZ AGURTO, NIBALDO

AU - Rubio, Jose

AU - Yañez, Eleuterio

PY - 2011/11/28

Y1 - 2011/11/28

N2 - In this paper, we use multi-scale stationary wavelet decomposition technique combined with a linear autoregressive model for one-month-ahead monthly sardine catches forecasting off central southern Chile.The monthly sardine catches data were collected from the database of the National Marine Fisheries Service for the period between 1 January 1964 and 30 December 2008. The proposed forecasting strategy is to decompose the raw sardine catches data set into trend component and residual component by using multi-scale stationary wavelet transform. In wavelet domain, both the trend component and the residual component are independently predicted using a linear autoregressive model. Hence, proposed forecaster is the co-addition of two predicted components. We find that the proposed forecasting method achieves a 99% of the explained variance with a reduced parsimonious and high accuracy.

AB - In this paper, we use multi-scale stationary wavelet decomposition technique combined with a linear autoregressive model for one-month-ahead monthly sardine catches forecasting off central southern Chile.The monthly sardine catches data were collected from the database of the National Marine Fisheries Service for the period between 1 January 1964 and 30 December 2008. The proposed forecasting strategy is to decompose the raw sardine catches data set into trend component and residual component by using multi-scale stationary wavelet transform. In wavelet domain, both the trend component and the residual component are independently predicted using a linear autoregressive model. Hence, proposed forecaster is the co-addition of two predicted components. We find that the proposed forecasting method achieves a 99% of the explained variance with a reduced parsimonious and high accuracy.

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RODRIGUEZ AGURTO NIBALDO, Rubio J, Yañez E. Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings. 2011. p. 654-663. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-25085-9_78

Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile

Abstract

Publication series

Conference

Keywords

UN SDGs

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