Monthly bigeye tuna catches forecasting usingwavelet functional autoregression

Nibaldo Rodriguez; Orlando Duran

doi:10.1109/WKDD.2010.139

Monthly bigeye tuna catches forecasting usingwavelet functional autoregression

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

Abstract

In this paper, the aim is to apply a functional autoregressive (FAR) model combined with multiscale wavelet analysis for monthly bigeye tuna catches forecasting in the ocean ecosystem of the equatorial Indian ocean. Wavelet technique performs a time-frequency analysis of a time series, which permits to decompose the raw time series into trend and residual components. In wavelet domain, the trend component and residual component are forecasted with a linear autoregressive model and a FAR model; respectively. Hence, the proposed forecast is the co-addition of two predicted components. We find that the proposed forecasting strategy achieves 98% of the explained variance with reduced parsimony and high accuracy.

Original language	English
Title of host publication	3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Pages	67-70
Number of pages	4
DOIs	https://doi.org/10.1109/WKDD.2010.139
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

Forecasting
Wavelet analysis

Access to Document

10.1109/WKDD.2010.139

Cite this

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title = "Monthly bigeye tuna catches forecasting usingwavelet functional autoregression",

abstract = "In this paper, the aim is to apply a functional autoregressive (FAR) model combined with multiscale wavelet analysis for monthly bigeye tuna catches forecasting in the ocean ecosystem of the equatorial Indian ocean. Wavelet technique performs a time-frequency analysis of a time series, which permits to decompose the raw time series into trend and residual components. In wavelet domain, the trend component and residual component are forecasted with a linear autoregressive model and a FAR model; respectively. Hence, the proposed forecast is the co-addition of two predicted components. We find that the proposed forecasting strategy achieves 98% of the explained variance with reduced parsimony and high accuracy.",

keywords = "Forecasting, Wavelet analysis",

author = "Nibaldo Rodriguez and Orlando Duran",

year = "2010",

doi = "10.1109/WKDD.2010.139",

language = "English",

isbn = "9780769539232",

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

pages = "67--70",

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 & Duran, O 2010, Monthly bigeye tuna catches forecasting usingwavelet functional autoregression. in 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010., 5432728, 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, pp. 67-70, 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, Phuket, Thailand, 9/01/10. https://doi.org/10.1109/WKDD.2010.139

Monthly bigeye tuna catches forecasting usingwavelet functional autoregression. / Rodriguez, Nibaldo ; Duran, Orlando.
3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010. 2010. p. 67-70 5432728 (3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010).

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

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AU - Rodriguez, Nibaldo

AU - Duran, Orlando

PY - 2010

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N2 - In this paper, the aim is to apply a functional autoregressive (FAR) model combined with multiscale wavelet analysis for monthly bigeye tuna catches forecasting in the ocean ecosystem of the equatorial Indian ocean. Wavelet technique performs a time-frequency analysis of a time series, which permits to decompose the raw time series into trend and residual components. In wavelet domain, the trend component and residual component are forecasted with a linear autoregressive model and a FAR model; respectively. Hence, the proposed forecast is the co-addition of two predicted components. We find that the proposed forecasting strategy achieves 98% of the explained variance with reduced parsimony and high accuracy.

AB - In this paper, the aim is to apply a functional autoregressive (FAR) model combined with multiscale wavelet analysis for monthly bigeye tuna catches forecasting in the ocean ecosystem of the equatorial Indian ocean. Wavelet technique performs a time-frequency analysis of a time series, which permits to decompose the raw time series into trend and residual components. In wavelet domain, the trend component and residual component are forecasted with a linear autoregressive model and a FAR model; respectively. Hence, the proposed forecast is the co-addition of two predicted components. We find that the proposed forecasting strategy achieves 98% of the explained variance with reduced parsimony and high accuracy.

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KW - Wavelet analysis

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DO - 10.1109/WKDD.2010.139

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

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Y2 - 9 January 2010 through 10 January 2010

ER -

Monthly bigeye tuna catches forecasting usingwavelet functional autoregression

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Keywords

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