In view of the complexity, low precision and low timeliness of traditional chaotic time series prediction
models, a model about full-parameters continued fraction is proposed on the basis of the inverse difference quotient continued fraction. The quantum particle swarm optimization algorithm is used for parameters optimization of the model, thus the parameters optimization problem is transformed into the function optimization problem on the multidimensional space. Second order forced Brussels vibrator and three-dimensional quadratic autonomous generalized Lorenz system are taken as models respectively, then chaotic time series which will be used as the simulation object can be attained according to the fourth order Runge-Kutta method. Intercomparison experiments among the model about full-parameters continued fraction based on the quantum particle swarm optimization algorithm, the BP neural network and the RBF neural network are conducted on single-step and multi-step prediction for chaotic time series. The simulation results show that the fullparameters continued fraction based on the quantum particle swarm optimization algorithm has simpler structure, higher precision and higher efficiency, so this prediction model can be widely promoted and applied.