A hybrid model for forecasting aquatic products short-term price integrated wavelet neural network with genetic algorithm

The technological advances in the production and storage of fishery products have exceeded the development of effective market demand over the past one-decade. As a result, participants within the fishery industry have frequently found themselves facing increased variable and declining prices negatively affected the fishery industry and need to be pro-active instead of reactive to market changes. In this paper, a hybrid model is described, which integrate the Wavelet Neural Network with Genetic Algorithm and can predict the short-term aquatic products price. Then the theory framework and algorithms of the model are discussed. Then an empirical example is described. It shows that the proposed model can predict the short-term aquatic product price with the scale of one day, one week and ten days and the precision of prediction is not the decline trend when the forecasting scale is extended.