@inproceedings{2fddc3623d6e426097799d0a680b15a3,
title = "Wavelet autoregressive model for monthly sardines catches forecasting off central southern Chile",
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.",
keywords = "autoregression, forecasting, wavelet decomposition",
author = "Nibaldo Rodriguez and Jose Rubio and Eleuterio Ya{\~n}ez",
year = "2011",
doi = "10.1007/978-3-642-25085-9_78",
language = "English",
isbn = "9783642250842",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "654--663",
booktitle = "Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Proceedings",
note = "null ; Conference date: 15-11-2011 Through 18-11-2011",
}