Evolutionary TARMA Modeling in Time Serials

Many phenomena in engineering applications, such as limit loop, resonated jumping phenomenon etc., can be modeled as non-linear models. Threshold Self Regression Model has been widely used in time series modeling because it can explain the phenomena cited above with physics meaning and has perfect performance in forecasting. In this paper, evolutionary TARMA modeling algorithm was proposed which can overcome some limitations of traditional methods including H. Tong method, D.D.C method and local research method. First the algorithm can automatically identify the type of model (linear or non-linear), order number of model and some relevant parameters (threshold interval parameter, threshold parameter and the corresponding parameters of ARMA model etc). The experiments show that the algorithm is effective, self-adaptive and robust. Moreover, the models constructed are abundant because of the existence of randomness, so decision makers can select appropriate model(s) to analyze time series or explain physically phenomenon.