Monthly anchovy catches forecasting using wavelet polynomial autoregression

Nibaldo Rodriguez, Eleuterio Yanez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojada3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Páginas126-129
Número de páginas4
DOI
EstadoPublicada - 2010
Publicado de forma externa
Evento3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010 - Phuket, Tailandia
Duración: 9 ene. 201010 ene. 2010

Serie de la publicación

Nombre3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010

Conferencia

Conferencia3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
País/TerritorioTailandia
CiudadPhuket
Período9/01/1010/01/10

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