International Workshop on Knowledge Discovery and Data Mining (2010)
Jan. 9, 2010 to Jan. 10, 2010
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WKDD.2010.139
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.
forecasting, wavelet analysis, autoregression
Nibaldo Rodriguez, Orlando Duran, "Monthly Bigeye Tuna Catches Forecasting Using Wavelet Functional Autoregression", International Workshop on Knowledge Discovery and Data Mining, vol. 00, no. , pp. 67-70, 2010, doi:10.1109/WKDD.2010.139