Undecimated wavelet based autoregressive model for anchovy catches forecasting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5317 LNAI, pages 325-332, 2008.
doi  abstract   bibtex   
The aim of this paper is to find a model to forecast 1-month ahead monthly anchovy catches using un-decimated multi-scale stationary wavelet transform (USWT) combined with linear autoregressive (AR) method. The original monthly anchovy catches are decomposed into various sub-series employing USWT and then appropriate sub-series are used as inputs to the multi-scale autoregressive (MAR) model. The MAR's parameters are estimated using the regularized least squares (RLS) method. RLS based forecasting performance was evaluated using determination coefficient and shown that a 99% of the explained variance was captured with a reduced parsimony and high accuracy. © 2008 Springer Berlin Heidelberg.
@inproceedings{10.1007/978-3-540-88636-531,
    abstract = "The aim of this paper is to find a model to forecast 1-month ahead monthly anchovy catches using un-decimated multi-scale stationary wavelet transform (USWT) combined with linear autoregressive (AR) method. The original monthly anchovy catches are decomposed into various sub-series employing USWT and then appropriate sub-series are used as inputs to the multi-scale autoregressive (MAR) model. The MAR's parameters are estimated using the regularized least squares (RLS) method. RLS based forecasting performance was evaluated using determination coefficient and shown that a 99\% of the explained variance was captured with a reduced parsimony and high accuracy. © 2008 Springer Berlin Heidelberg.",
    year = "2008",
    title = "Undecimated wavelet based autoregressive model for anchovy catches forecasting",
    volume = "5317 LNAI",
    pages = "325-332",
    doi = "10.1007/978-3-540-88636-531",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"
}

Downloads: 0