Monthly anchovy catches forecasting using wavelet polynomial autoregression

Nibaldo Rodriguez; Eleuterio Yanez

doi:10.1109/WKDD.2010.140

Monthly anchovy catches forecasting using wavelet polynomial autoregression

Nibaldo Rodriguez, Eleuterio Yanez

SCHOOL OF COMPUTER ENGINEERING

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

Abstract

In this paper, a multivariate polynomial (MP) model combined with wavelet analysis is proposed to improve the accuracy and parsimony of 1-month ahead forecasting of monthly anchovy catches in northern Chile. The proposed forecasting model is based on the decomposition the raw data set into low frequency (LF) and high frequency (HF) components by using stationary wavelet transform. In wavelet domain, the LF component and HF component are predicted with a linear autoregressive model and multiscale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy. Besides, the proposed forecaster proves to be more accurate and performs better than the multilayer perceptron neural network model.

Original language	English
Title of host publication	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Pages	126-129
Number of pages	4
DOIs	https://doi.org/10.1109/WKDD.2010.140
State	Published - 2010
Event	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 - Phuket, Thailand Duration: 9 Jan 2010 → 10 Jan 2010

Publication series

Name	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

Conference

Conference	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Country/Territory	Thailand
City	Phuket
Period	9/01/10 → 10/01/10

Keywords

Autoregression
Forecasting
Wavelet analysis

Access to Document

10.1109/WKDD.2010.140

Cite this

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title = "Monthly anchovy catches forecasting using wavelet polynomial autoregression",

abstract = "In this paper, a multivariate polynomial (MP) model combined with wavelet analysis is proposed to improve the accuracy and parsimony of 1-month ahead forecasting of monthly anchovy catches in northern Chile. The proposed forecasting model is based on the decomposition the raw data set into low frequency (LF) and high frequency (HF) components by using stationary wavelet transform. In wavelet domain, the LF component and HF component are predicted with a linear autoregressive model and multiscale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy. Besides, the proposed forecaster proves to be more accurate and performs better than the multilayer perceptron neural network model.",

keywords = "Autoregression, Forecasting, Wavelet analysis",

author = "Nibaldo Rodriguez and Eleuterio Yanez",

year = "2010",

doi = "10.1109/WKDD.2010.140",

language = "English",

isbn = "9780769539232",

series = "3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010",

pages = "126--129",

booktitle = "3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010",

note = "3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 ; Conference date: 09-01-2010 Through 10-01-2010",

}

Rodriguez, N & Yanez, E 2010, Monthly anchovy catches forecasting using wavelet polynomial autoregression. in 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010., 5432705, 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, pp. 126-129, 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, Phuket, Thailand, 9/01/10. https://doi.org/10.1109/WKDD.2010.140

Monthly anchovy catches forecasting using wavelet polynomial autoregression. / Rodriguez, Nibaldo; Yanez, Eleuterio.
3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010. 2010. p. 126-129 5432705 (3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010).

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

TY - GEN

T1 - Monthly anchovy catches forecasting using wavelet polynomial autoregression

AU - Rodriguez, Nibaldo

AU - Yanez, Eleuterio

PY - 2010

Y1 - 2010

N2 - In this paper, a multivariate polynomial (MP) model combined with wavelet analysis is proposed to improve the accuracy and parsimony of 1-month ahead forecasting of monthly anchovy catches in northern Chile. The proposed forecasting model is based on the decomposition the raw data set into low frequency (LF) and high frequency (HF) components by using stationary wavelet transform. In wavelet domain, the LF component and HF component are predicted with a linear autoregressive model and multiscale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy. Besides, the proposed forecaster proves to be more accurate and performs better than the multilayer perceptron neural network model.

AB - In this paper, a multivariate polynomial (MP) model combined with wavelet analysis is proposed to improve the accuracy and parsimony of 1-month ahead forecasting of monthly anchovy catches in northern Chile. The proposed forecasting model is based on the decomposition the raw data set into low frequency (LF) and high frequency (HF) components by using stationary wavelet transform. In wavelet domain, the LF component and HF component are predicted with a linear autoregressive model and multiscale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy. Besides, the proposed forecaster proves to be more accurate and performs better than the multilayer perceptron neural network model.

KW - Autoregression

KW - Forecasting

KW - Wavelet analysis

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M3 - Conference contribution

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T3 - 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

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BT - 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

T2 - 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

Y2 - 9 January 2010 through 10 January 2010

ER -

Monthly anchovy catches forecasting using wavelet polynomial autoregression

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Keywords

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