Continuous ant colony optimization algorithms in a support vector regression based financial forecasting model

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination for a SVR model is competent to the forecasting accuracy. Several evolutionary algorithms, such as genetic algorithms and simulated annealing algorithms have been used to the parameters selection, however, these algorithms often suffer the problem of being trapped in local optimum. This investigation used continuous ant colony optimization algorithms in a SVR model for selecting suitable parameters, in which encouraging local search in areas where forecasting accuracy improvement continues to be made, then, autocatalytically converge to promising regions. Numerical examples of exchange rates forecasting from an existing literature are employed to compare the performance of the proposed model. Experiment results show that the proposed model outperforms the other approaches in the literature.