Prediction about time series based on updated prediction ARMA model

This paper proposes an updated prediction ARMA (autoregressive moving average) model for the disadvantage of traditional model that the future value forecasted by k-step ahead predictive model from time t didn´t include the newest information on time t + 1 with the passage of time after a model was build. For this purpose, we adapt an approach of combining the ARMA model´s difference equation form and transfer form (with Green´s function) to achieve that new prediction value will calculate the change of the newest observation instead of reestablishing a new model. Furthermore, this method obtains higher forecasting accuracy and less computation. Finally we take an experiment on a time series sequence data to indicate the model´s efficiency and effectiveness.