Wavelet Polynomial Autoregression for Monthly Bigeye Tuna Catches Forecasting

In this paper, multiscale wavelet analysis combined with a multivariate polynomial is presented to improve the accuracy and parsimony of 1-month ahead forecasting of monthly bigeye tuna catches in equatorial Indian Ocean. The proposed forecasting model is based on the decomposition the raw data set into trend and residuals components by using stationary wavelet transform. In wavelet domain, the trend component and residuals components are predicted with a linear autoregressive model and a multi-scale polynomial autoregressive model; respectively. We find that the proposed forecasting method achieves 99% of the explained variance with reduced parsimony and high accuracy.