Abstract: Abstract: Fluctuations in aquatic product prices have an important impact on the development of the aquaculture industry. Accurate forecasting results can enable farmers to keep abreast of changes in the market and rationally plan aquaculture. Based on the non-linear, non-stationary and periodicity of the aquatic product price series, a genetic algorithm (GA) support vector regression (SVR) model based on time series for forecasting aquatic product price was presented in this paper. Firstly, the time series method was applied to the price series, the autocorrelation function was used to judge the stability, and the partial correlation coefficient was used to judge the data items, then the training data set was obtained. After that, the genetic algorithm was used to optimize the parameters of support vector regression. The parameters of SVR based on radial basis kernel function were kernel function coefficient, penalty factor, and loss parameter. We designed these three parameters by using real number coding individual representation. We used the selection operation to select the mean square error as the fitness function, to calculate the fitness value of each individual, and to select the individuals with better fitness value. By use of the crossover operation, we selected the point intersecting as crossover operator with different individuals, respectively in a corresponding position to a certain probability. The nature of mutation operation was used to enhance the local search algorithm, and avoid falling into the local minimum. We mutated individual to a certain probability and change its current value, then generated new population. We introduced the mechanism of 5-fold cross validation to the process of each iteration to obtain the optimized parameter combination. Finally, the support vector regression model was established by using the optimized parameters to forecast the price of aquatic products in the next period. In this paper, we selected mandarin fish, metapenaeus ensis and portunus trituberculatus as the experimental objects. The experimental data we used were the value of aquatic product price from January 2011 to December 2015 of Beijing Xinfadi market website (http://www.xinfadi.com.cn). After craw ling the web data-including 1,541 records of mandarin fish, 1,525 records of metapenaeus ensis and 1,430 records of portunus trituberculatus, we calculated the monthly average price to represent the price of a period. We trained the proposed model by using data from 2011 to 2014, and forecasted the price of the next year. Through comparing with the real value, the mean absolute percent error of mandarin fish, metapenaeus ensis and portunus trituberculatus was 6.70%, 7.82% and 14.76%, with corresponding root mean square error of 5.8531, 23.7011 and 13.8580, respectively. After surveying the market, we found that the results of forecasting were more in line with the actual situation. In this paper, the SVR model and the BP neural network model based on time series were all used in contrast experiment of our model. The experiment results showed that our model was superior. According to the characteristics of aquatic product price in this paper, we proposed a combined model for the determination of the relevant items of the aquatic product price series, the selection of the kernel function and the parameter optimization. The results showed that the proposed model can provide the basis for the forecasting of aquatic product price.