Progressive autoregressive prediction method

A progressive autoregressive prediction method is presented, which includes the progressive autoregression (PAR), the progressive autoregressive moving average (PARMA), the progressive time-varying autoregression (PTVAR), the progressive time-varying autoregressive moving average (PTVARMA), the progressive regression and autoregression (PRAR) model. Their prediction formulas, mean square errors and confidence interval estimates are established. In this method, the autoregressive, the moving average and the regressive coefficients are corrected according to previous predicting results, and the next prediction can have higher precision than traditional method. As the method is nonlinear prediction, the PAR and the PARMA model are different from the autoregression (AR) model and the autoregressive moving average (ARMA) model, which can be used to predict the covariance stationarity, the nonstationarity, and the deterministic series. Furthermore, the regularity of test data can be greatly enhanced by weighted accumulative addition, reciprocal transformation and so on, which raises the precision of the prediction in time series.