Time series prediction model building with BP-like parameter optimization

A method for building time series prediction model using genetic programming is proposed. The construction of prediction models consists of two stages. The first stage composes the most appropriate functional form with genetic programming. The second stage fixes optimal parameters involved in the composite function with a backpropagation-like algorithm. The second stage can be recognized as a local search, which is a powerful tool to accelerate the evolving speed of GP and GA. The method is applied to typical time series and some real world prediction problems. Results of computer generated chaotic time series were compared to those of neural network based and autoregressive predictions. The superiority of the proposed method is demonstrated in the results of these experiments