financial timeseries prediction by a hybrid model of chaos theory, multi-layer perceptron and metaheuristic algorithm

Many researchers proved that hybrid models have better results in comparison with independent models. a combination of different methods could enhance the accuracy of time series prediction. hence, this research used the hybrid of three methods of chaos theory, multi-layer perceptron and metaheuristic algorithm to increase the power of the model forecasting. artificial neural networks have properly considered complex nonlinear relations and are good comprehensive approximators. multi-objective evolutionary algorithms such as multi-objective particle swarm optimization are good at solving multi-objective optimization issues. this algorithm organized the combination of parent and children populations by elitist strategy, decreased the messy comparing factors to improve the solution variety and avoided to use of niche factors. chaos theory controls the complexities of stochastic systems. so, this research offers tehran stock exchange index (tsei) prediction by a hybrid model of chaos theory, multi-layer perceptron and metaheuristic algorithm. the results show that in perceptron-based mode, rmse measures are gradually increased in all intervals. the continuous decrease of rmse shows that the perceptron-based model could show consistency with the whole data flow. this matter could offer a better learning and consistency process by perceptron-based models to predict stock prices, as this type of learning could apply more experiences for forecasting future behaviour in order to change the system content.