ARMA model

ABSTRACT

This paper propose an updated prediction ARMA (autoregressive moving average) model for the disadvantage of traditional model that the further value forecasted by k-step ahead predictive model form 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.